Scanpy Analysis

%config InlineBackend.figure_formats = ["retina"]  # increase resolution on retina screens
import numpy as np
import pandas as pd
import warnings
import sys
warnings.filterwarnings("ignore")
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
#import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.colors
import scanpy as sc
import seaborn as sns
import anndata as ad
sc.settings.verbosity = 3             # verbosity: errors (0), warnings (1), info (2), hints (3)
sc.logging.print_header()
sc.settings.n_jobs = 23

scanpy==1.9.1 anndata==0.9.2 umap==0.5.3 numpy==1.23.5 scipy==1.11.1 pandas==1.5.2 scikit-learn==1.3.0 statsmodels==0.14.0 python-igraph==0.11.5 pynndescent==0.5.10

from gtfparse import read_gtf

%load_ext rpy2.ipython

INFO:rpy2.situation:cffi mode is CFFI_MODE.ANY
INFO:rpy2.situation:R home found: /scratch/prj/sangrithi_lab/programs/miniconda3/envs/MS_Conda5/lib/R
INFO:rpy2.situation:R library path: 
INFO:rpy2.situation:LD_LIBRARY_PATH: 
INFO:rpy2.rinterface_lib.embedded:Default options to initialize R: rpy2, --quiet, --no-save
INFO:rpy2.rinterface_lib.embedded:R is already initialized. No need to initialize.

pd.set_option('display.max_columns', 500)
pd.set_option('display.max_rows', 100)#None

plt.rcParams.update({})
# Say, "the default sans-serif font is COMIC SANS"
matplotlib.rcParams['font.sans-serif'] = "Comic Sans MS"
# Then, "ALWAYS use sans-serif fonts"
matplotlib.rcParams['font.family'] = "sans-serif"
sc.settings.set_figure_params(dpi=125, dpi_save=300, format="pdf", frameon=False, vector_friendly=True)
sc.settings.figdir = '/users/k2142342/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_13May24/Figures/'
# Set context to `"paper"`
sns.set_context("paper", font_scale=1, rc={"font.size":8,"axes.labelsize":8,"axes.titlesize":14})

heatcolors_wr = matplotlib.colors.LinearSegmentedColormap.from_list("", ["white", "#FFF5F0", "#FEE0D2", "#FCBBA1", 
                                                                         "#FC9272", "#FB6A4A", "#EF3B2C", 
                                                                         "#CB181D", "#A50F15", "#67000D"])
heatcolors = matplotlib.colors.LinearSegmentedColormap.from_list("", ["gray", "#000004FF", "#330A5FFF", "#781C6DFF",
                                                                      "#BB3754FF", "#ED6925FF", 
                                                                      "#FCB519FF", "#FCFFA4FF"])

#%%R
#heatcolour_wr <- colorRampPalette(c("white", "#FFF5F0", "#FEE0D2", "#FCBBA1","#FC9272", "#FB6A4A", "#EF3B2C","#CB181D", "#A50F15", "#67000D"))(100)
#show_col(heatcolour_wr)

%%R
require(tidyverse)
require(dplyr)
require(tidyr)
options(mc.cores = 23L)
require(magrittr)
#library(scatterpie)
require(extrafont)
require(scales)
require(viridis)
require(viridisLite)
require(gridExtra)
options(dplyr.width = 200)
#options(repr.matrix.max.cols=500, repr.matrix.max.rows=100)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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✔ forcats   1.0.0     ✔ stringr   1.5.0
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✔ lubridate 1.9.2     ✔ tidyr     1.3.0
✔ purrr     1.0.1     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Loading required package: tidyverse
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Attaching package: ‘magrittr’

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Attaching package: ‘scales’

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Loading required package: viridis
Loading required package: viridisLite

Attaching package: ‘viridis’

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Loading required package: gridExtra

Attaching package: ‘gridExtra’

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## Load the Mouse GTF file
gtf_df = read_gtf("/users/k2142342/Lab_Sangrithi/reference/annot/Mus_musculus.GRCm38/Mus_musculus.GRCm38.102.chr.gtf")

INFO:root:Extracted GTF attributes: ['gene_id', 'gene_version', 'gene_name', 'gene_source', 'gene_biotype', 'havana_gene', 'havana_gene_version', 'transcript_id', 'transcript_version', 'transcript_name', 'transcript_source', 'transcript_biotype', 'havana_transcript', 'havana_transcript_version', 'tag', 'transcript_support_level', 'exon_number', 'exon_id', 'exon_version', 'ccds_id', 'protein_id', 'protein_version']

genes = gtf_df[gtf_df.transcript_biotype.isin(['protein_coding',
       'antisense', 'lincRNA',
       'TR_V_gene',
       'TR_V_pseudogene', 'TR_D_gene', 'TR_J_gene', 'TR_C_gene',
       'TR_J_pseudogene', 'IG_LV_gene', 'IG_V_gene', 'IG_V_pseudogene',
       'IG_J_gene', 'IG_C_gene', 'IG_C_pseudogene',
       'IG_D_gene', 'IG_D_pseudogene', 'IG_pseudogene'])].gene_id.unique()

len(genes)

31434

genes_df = gtf_df[gtf_df.transcript_biotype.isin(['protein_coding',
       'antisense', 'lincRNA',
        'TR_V_gene',
       'TR_V_pseudogene', 'TR_D_gene', 'TR_J_gene', 'TR_C_gene',
       'TR_J_pseudogene', 'IG_LV_gene', 'IG_V_gene', 'IG_V_pseudogene',
       'IG_J_gene', 'IG_C_gene', 'IG_C_pseudogene',
       'IG_D_gene', 'IG_D_pseudogene', 'IG_pseudogene'])].reset_index(drop = True)

genes_df

	seqname	source	feature	start	end	score	strand	frame	gene_id	gene_version	gene_name	gene_source	gene_biotype	havana_gene	havana_gene_version	transcript_id	transcript_version	transcript_name	transcript_source	transcript_biotype	havana_transcript	havana_transcript_version	tag	transcript_support_level	exon_number	exon_id	exon_version	ccds_id	protein_id	protein_version
0	1	ensembl_havana	transcript	3214482	3671498	NaN	-	0	ENSMUSG00000051951	5	Xkr4	ensembl_havana	protein_coding	OTTMUSG00000026353	2	ENSMUST00000070533	4	Xkr4-201	ensembl_havana	protein_coding	OTTMUST00000065166	1	CCDS,basic	1				CCDS14803
1	1	ensembl_havana	exon	3670552	3671498	NaN	-	0	ENSMUSG00000051951	5	Xkr4	ensembl_havana	protein_coding	OTTMUSG00000026353	2	ENSMUST00000070533	4	Xkr4-201	ensembl_havana	protein_coding	OTTMUST00000065166	1	CCDS,basic	1	1	ENSMUSE00000485541	3	CCDS14803
2	1	ensembl_havana	CDS	3670552	3671348	NaN	-	0	ENSMUSG00000051951	5	Xkr4	ensembl_havana	protein_coding	OTTMUSG00000026353	2	ENSMUST00000070533	4	Xkr4-201	ensembl_havana	protein_coding	OTTMUST00000065166	1	CCDS,basic	1	1			CCDS14803	ENSMUSP00000070648	4
3	1	ensembl_havana	start_codon	3671346	3671348	NaN	-	0	ENSMUSG00000051951	5	Xkr4	ensembl_havana	protein_coding	OTTMUSG00000026353	2	ENSMUST00000070533	4	Xkr4-201	ensembl_havana	protein_coding	OTTMUST00000065166	1	CCDS,basic	1	1			CCDS14803
4	1	ensembl_havana	exon	3421702	3421901	NaN	-	0	ENSMUSG00000051951	5	Xkr4	ensembl_havana	protein_coding	OTTMUSG00000026353	2	ENSMUST00000070533	4	Xkr4-201	ensembl_havana	protein_coding	OTTMUST00000065166	1	CCDS,basic	1	2	ENSMUSE00000449517	3	CCDS14803
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1387326	MT	insdc	stop_codon	13552	13554	NaN	-	0	ENSMUSG00000064368	1	mt-Nd6	insdc	protein_coding			ENSMUST00000082419	1	mt-Nd6-201	insdc	protein_coding			basic	NA	1
1387327	MT	insdc	transcript	14145	15288	NaN	+	0	ENSMUSG00000064370	1	mt-Cytb	insdc	protein_coding			ENSMUST00000082421	1	mt-Cytb-201	insdc	protein_coding			basic	NA
1387328	MT	insdc	exon	14145	15288	NaN	+	0	ENSMUSG00000064370	1	mt-Cytb	insdc	protein_coding			ENSMUST00000082421	1	mt-Cytb-201	insdc	protein_coding			basic	NA	1	ENSMUSE00000521548	1
1387329	MT	insdc	CDS	14145	15288	NaN	+	0	ENSMUSG00000064370	1	mt-Cytb	insdc	protein_coding			ENSMUST00000082421	1	mt-Cytb-201	insdc	protein_coding			basic	NA	1				ENSMUSP00000081003	1
1387330	MT	insdc	start_codon	14145	14147	NaN	+	0	ENSMUSG00000064370	1	mt-Cytb	insdc	protein_coding			ENSMUST00000082421	1	mt-Cytb-201	insdc	protein_coding			basic	NA	1

1387331 rows × 30 columns

#genes_df.to_csv("/users/k2142342/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_9Feb23/genes_df.csv")

#genes_df = pd.read_csv("/users/k2142342/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_9Feb23/genes_df.csv", index_col=0)

genes = genes_df.gene_id.unique()

len(genes_df.gene_id[genes_df.seqname.isin(['MT'])].unique())

13

%%R -o countdataSC
# Load the count data
countdataSC <- read.csv("~/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_13May24/gcAnndata_raw_x.csv", header=TRUE, row.names=1)
dim(countdataSC)

[1] 31434   822

%%R
colnames(countdataSC) %>% head() %>% print()
dim(countdataSC)
colnames(countdataSC) <- gsub("\\.", "_", colnames(countdataSC))
dim(countdataSC)

[1] "E10_GFP_pos_F1_10" "E10_GFP_pos_F1_11" "E10_GFP_pos_F1_12"
[4] "E10_GFP_pos_F1_14" "E10_GFP_pos_F1_15" "E10_GFP_pos_F1_16"
[1] 31434   822

%%R
colnames(countdataSC) %>% head() %>% print()
#head(countdataSC)

[1] "E10_GFP_pos_F1_10" "E10_GFP_pos_F1_11" "E10_GFP_pos_F1_12"
[4] "E10_GFP_pos_F1_14" "E10_GFP_pos_F1_15" "E10_GFP_pos_F1_16"

%%R -o countdataSCMeta
countdataSCMeta <- read.csv("~/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_13May24/gcAnndata_obs.csv",
                             header = T,
                             row.names = 1)


countdataSCMeta <- countdataSCMeta[,-c(2,3,4,5,13,14)]
#rownames(countdataSCMeta) <- countdataSCMeta$sample
#colnames(countdataSCMeta)[1] = "Cell"

%%R
countdataSCMeta %>% names() #group_by(age, sex) %>% tally()

[1] "Sample"             "sex"                "stage"             
[4] "Embryo"             "celltype"           "stage_sex"         
[7] "stage_sex_celltype" "CellID"            

%%R -o countdataSC -o obs_names -o var_names -i genes
# subset rownames
countdataSC <- countdataSC[rownames(countdataSC) %in% genes, ]
obs_names <- colnames(countdataSC)
var_names <- rownames(countdataSC)
countdataSC <- as.matrix(countdataSC)
print(dim(countdataSC[rownames(countdataSC) %in% genes, ]))

[1] 31434   822

Genome1 and 2 counts

%%R -o countdataSC_g1 -o countdataSC_g2
countdataSC_g1 <- read.table("~/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_13May24/Updated_counts/counts_genome1.txt", header=TRUE, row.names=1)
countdataSC_g2 <- read.table("~/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_13May24/Updated_counts/counts_genome2.txt", header=TRUE, row.names=1)

countdataSC_g1 <- countdataSC_g1[,-c(1:5)]
countdataSC_g2 <- countdataSC_g2[,-c(1:5)]

%%R
colnames(countdataSC_g1) <- gsub("\\.", "_", colnames(countdataSC_g1))
colnames(countdataSC_g2) <- gsub("\\.", "_", colnames(countdataSC_g2))

dim(countdataSC_g1) %>% print()
dim(countdataSC_g2) %>% print()

[1] 55401   882
[1] 55401   882

%%R -o countdataSC_g1 -o obs_names_g1 -o var_names_g1 -i genes
# subset rownames
countdataSC_g1 <- countdataSC_g1[rownames(countdataSC_g1) %in% genes, ]
obs_names_g1 <- colnames(countdataSC_g1)
var_names_g1 <- rownames(countdataSC_g1)
countdataSC_g1 <- as.matrix(countdataSC_g1)
print(dim(countdataSC_g1[rownames(countdataSC_g1) %in% genes, ]))

[1] 31434   882

%%R 
#head(countdataSC_g1) 
colnames(countdataSC_g1) %in% rownames(countdataSCMeta) %>% table()
#colnames(countdataSC_g1)

.
FALSE 
  882 

%%R -o countdataSC_g2 -o obs_names_g2 -o var_names_g2 -i genes
# subset rownames
countdataSC_g2 <- countdataSC_g2[rownames(countdataSC_g2) %in% genes, ]
obs_names_g2 <- colnames(countdataSC_g2)
var_names_g2 <- rownames(countdataSC_g2)
countdataSC_g2 <- as.matrix(countdataSC_g2)
print(dim(countdataSC_g2[rownames(countdataSC_g2) %in% genes, ]))

[1] 31434   882

%%R
is.na(countdataSC_g1) %>% table()
is.na(countdataSC_g2) %>% table()

.
   FALSE 
27724788 

SCANPY

del gcAnndata del gcAnndata_f del gcAnndata_germ del gcAnndata_som

gcAnndata = sc.AnnData(X = countdataSC.T, obs = obs_names, var = var_names)

gcAnndata.obs.rename(columns={0:'Sample'}, inplace=True)
gcAnndata.var.rename(columns={0:'geneid'}, inplace=True)

gcAnndata.var.index = gcAnndata.var.geneid.to_list()
gcAnndata.obs.index = gcAnndata.obs.Sample.to_list()

annot = sc.queries.biomart_annotations(
    "mmusculus",
    ["ensembl_gene_id", "start_position", "end_position", "chromosome_name", "external_gene_name"],
    host="nov2020.archive.ensembl.org",
    use_cache='TRUE',
    ).set_index("ensembl_gene_id")

gcAnndata.var[annot.columns] = annot

gcAnndata.var.index = gcAnndata.var.geneid.tolist()

gcAnndata

AnnData object with n_obs × n_vars = 822 × 31434
    obs: 'Sample'
    var: 'geneid', 'start_position', 'end_position', 'chromosome_name', 'external_gene_name'

gcAnndata.var_names_make_unique()

gcAnndata.n_obs

822

gcAnndata.var

	geneid	start_position	end_position	chromosome_name	external_gene_name
ENSMUSG00000051951	ENSMUSG00000051951	3205901	3671498	1	Xkr4
ENSMUSG00000089699	ENSMUSG00000089699	3466587	3513553	1	Gm1992
ENSMUSG00000102343	ENSMUSG00000102343	3905739	3986215	1	Gm37381
ENSMUSG00000025900	ENSMUSG00000025900	3999557	4409241	1	Rp1
ENSMUSG00000025902	ENSMUSG00000025902	4490931	4497354	1	Sox17
...	...	...	...	...	...
ENSMUSG00000065947	ENSMUSG00000065947	9877	10173	MT	mt-Nd4l
ENSMUSG00000064363	ENSMUSG00000064363	10167	11544	MT	mt-Nd4
ENSMUSG00000064367	ENSMUSG00000064367	11742	13565	MT	mt-Nd5
ENSMUSG00000064368	ENSMUSG00000064368	13552	14070	MT	mt-Nd6
ENSMUSG00000064370	ENSMUSG00000064370	14145	15288	MT	mt-Cytb

31434 rows × 5 columns

gcAnndata.var.chromosome_name.str.startswith('MT')

ENSMUSG00000051951    False
ENSMUSG00000089699    False
ENSMUSG00000102343    False
ENSMUSG00000025900    False
ENSMUSG00000025902    False
                      ...  
ENSMUSG00000065947     True
ENSMUSG00000064363     True
ENSMUSG00000064367     True
ENSMUSG00000064368     True
ENSMUSG00000064370     True
Name: chromosome_name, Length: 31434, dtype: bool

gcAnndata.var['mt'] = gcAnndata.var.chromosome_name=='MT' # annotate the group of mitochondrial genes as 'mt'
gcAnndata.var['mt']

ENSMUSG00000051951    False
ENSMUSG00000089699    False
ENSMUSG00000102343    False
ENSMUSG00000025900    False
ENSMUSG00000025902    False
                      ...  
ENSMUSG00000065947     True
ENSMUSG00000064363     True
ENSMUSG00000064367     True
ENSMUSG00000064368     True
ENSMUSG00000064370     True
Name: mt, Length: 31434, dtype: bool

gcAnndata.var.chromosome_name.str.startswith('MT')

ENSMUSG00000051951    False
ENSMUSG00000089699    False
ENSMUSG00000102343    False
ENSMUSG00000025900    False
ENSMUSG00000025902    False
                      ...  
ENSMUSG00000065947     True
ENSMUSG00000064363     True
ENSMUSG00000064367     True
ENSMUSG00000064368     True
ENSMUSG00000064370     True
Name: chromosome_name, Length: 31434, dtype: bool

sc.pp.calculate_qc_metrics(gcAnndata, qc_vars=['mt'], percent_top=None, log1p=False, inplace=True)

gcAnndata.obs

	Sample	n_genes_by_counts	total_counts	total_counts_mt	pct_counts_mt
E10_GFP_pos_F1_10	E10_GFP_pos_F1_10	8707	715292	918	0.128339
E10_GFP_pos_F1_11	E10_GFP_pos_F1_11	8473	1564689	5792	0.370169
E10_GFP_pos_F1_12	E10_GFP_pos_F1_12	8206	2310070	3992	0.172809
E10_GFP_pos_F1_14	E10_GFP_pos_F1_14	8338	1320374	5065	0.383603
E10_GFP_pos_F1_15	E10_GFP_pos_F1_15	8473	1283668	3195	0.248896
...	...	...	...	...	...
M4_GFPP_P2_B5	M4_GFPP_P2_B5	5349	1735796	15873	0.914451
M4_GFPP_P2_B7	M4_GFPP_P2_B7	6595	2647587	22935	0.866260
M4_GFPP_P2_B8	M4_GFPP_P2_B8	5555	1920124	20754	1.080868
M4_GFPP_P2_B9	M4_GFPP_P2_B9	5391	1287828	9147	0.710266
M4_GFPP_P2_D2	M4_GFPP_P2_D2	6042	3026218	22180	0.732928

822 rows × 5 columns

countdataSCMeta

	Sample	sex	stage	Embryo	celltype	stage_sex	stage_sex_celltype	CellID
E10_GFP_pos_F1_10	E10_GFP_pos_F1_10	female	E10.5	F1	germ_cell	female_E10.5	female_E10.5_germ_cell	E10_GFP_pos_F1_10
E10_GFP_pos_F1_11	E10_GFP_pos_F1_11	female	E10.5	F1	germ_cell	female_E10.5	female_E10.5_germ_cell	E10_GFP_pos_F1_11
E10_GFP_pos_F1_12	E10_GFP_pos_F1_12	female	E10.5	F1	germ_cell	female_E10.5	female_E10.5_germ_cell	E10_GFP_pos_F1_12
E10_GFP_pos_F1_14	E10_GFP_pos_F1_14	female	E10.5	F1	germ_cell	female_E10.5	female_E10.5_germ_cell	E10_GFP_pos_F1_14
E10_GFP_pos_F1_15	E10_GFP_pos_F1_15	female	E10.5	F1	germ_cell	female_E10.5	female_E10.5_germ_cell	E10_GFP_pos_F1_15
...	...	...	...	...	...	...	...	...
E115_GFP_neg_M5_5	E115_GFP_neg_M5_5	male	E11.5	M5	somatic_cell	male_E11.5	male_E11.5_somatic_cell	E115_GFP_neg_M5_5
E115_GFP_neg_M5_6	E115_GFP_neg_M5_6	male	E11.5	M5	somatic_cell	male_E11.5	male_E11.5_somatic_cell	E115_GFP_neg_M5_6
E115_GFP_neg_M5_7	E115_GFP_neg_M5_7	male	E11.5	M5	somatic_cell	male_E11.5	male_E11.5_somatic_cell	E115_GFP_neg_M5_7
E115_GFP_neg_M5_8	E115_GFP_neg_M5_8	male	E11.5	M5	somatic_cell	male_E11.5	male_E11.5_somatic_cell	E115_GFP_neg_M5_8
E115_GFP_neg_M5_9	E115_GFP_neg_M5_9	male	E11.5	M5	somatic_cell	male_E11.5	male_E11.5_somatic_cell	E115_GFP_neg_M5_9

822 rows × 8 columns

gcAnndata.obs['sex'] = countdataSCMeta['sex']

gcAnndata.obs['stage'] = countdataSCMeta['stage']

gcAnndata.obs['Embryo'] = countdataSCMeta['Embryo']
gcAnndata.obs['celltype'] = countdataSCMeta['celltype']

gcAnndata.obs['stage_sex'] = gcAnndata.obs.sex.astype('str') + "_" + gcAnndata.obs.stage.astype('str')
gcAnndata.obs['stage_sex_celltype'] = gcAnndata.obs.stage_sex.astype('str') + "_" + gcAnndata.obs.celltype.astype('str')

#pd.set_option('display.max_rows', 10)
gcAnndata.obs

	Sample	n_genes_by_counts	total_counts	total_counts_mt	pct_counts_mt	sex	stage	Embryo	celltype	stage_sex	stage_sex_celltype
E10_GFP_pos_F1_10	E10_GFP_pos_F1_10	8707	715292	918	0.128339	female	E10.5	F1	germ_cell	female_E10.5	female_E10.5_germ_cell
E10_GFP_pos_F1_11	E10_GFP_pos_F1_11	8473	1564689	5792	0.370169	female	E10.5	F1	germ_cell	female_E10.5	female_E10.5_germ_cell
E10_GFP_pos_F1_12	E10_GFP_pos_F1_12	8206	2310070	3992	0.172809	female	E10.5	F1	germ_cell	female_E10.5	female_E10.5_germ_cell
E10_GFP_pos_F1_14	E10_GFP_pos_F1_14	8338	1320374	5065	0.383603	female	E10.5	F1	germ_cell	female_E10.5	female_E10.5_germ_cell
E10_GFP_pos_F1_15	E10_GFP_pos_F1_15	8473	1283668	3195	0.248896	female	E10.5	F1	germ_cell	female_E10.5	female_E10.5_germ_cell
...	...	...	...	...	...	...	...	...	...	...	...
M4_GFPP_P2_B5	M4_GFPP_P2_B5	5349	1735796	15873	0.914451	male	E12.5	M4	germ_cell	male_E12.5	male_E12.5_germ_cell
M4_GFPP_P2_B7	M4_GFPP_P2_B7	6595	2647587	22935	0.866260	male	E12.5	M4	germ_cell	male_E12.5	male_E12.5_germ_cell
M4_GFPP_P2_B8	M4_GFPP_P2_B8	5555	1920124	20754	1.080868	male	E12.5	M4	germ_cell	male_E12.5	male_E12.5_germ_cell
M4_GFPP_P2_B9	M4_GFPP_P2_B9	5391	1287828	9147	0.710266	male	E12.5	M4	germ_cell	male_E12.5	male_E12.5_germ_cell
M4_GFPP_P2_D2	M4_GFPP_P2_D2	6042	3026218	22180	0.732928	male	E12.5	M4	germ_cell	male_E12.5	male_E12.5_germ_cell

822 rows × 11 columns

#gcAnndata[gcAnndata.obs['Sample'].isin(['E145_2F_P2_B4', 'E145_2F_P2_B5', 'E145_2F_P2_B6', 'E125_5F_GFPP_B11'])].obs.head()
#gcAnndata[gcAnndata.obs['stage'].isin(['E16_5'])].obs.head()
gcAnndata

AnnData object with n_obs × n_vars = 822 × 31434
    obs: 'Sample', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'sex', 'stage', 'Embryo', 'celltype', 'stage_sex', 'stage_sex_celltype'
    var: 'geneid', 'start_position', 'end_position', 'chromosome_name', 'external_gene_name', 'mt', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts'

gcAnndata.obs['stage_sex_celltype'].value_counts()

female_E16.5_germ_cell       71
male_E16.5_germ_cell         67
male_E15.5_germ_cell         65
male_E14.5_germ_cell         48
female_E12.5_germ_cell       48
female_E14.5_germ_cell       48
female_E10.5_germ_cell       47
female_E15.5_germ_cell       47
male_E9.5_germ_cell          47
female_E11.5_germ_cell       47
female_E9.5_germ_cell        46
male_E13.5_germ_cell         45
male_E11.5_germ_cell         44
female_E13.5_germ_cell       40
male_E12.5_germ_cell         40
male_E10.5_germ_cell         24
male_E11.5_somatic_cell      24
female_E11.5_somatic_cell    24
Name: stage_sex_celltype, dtype: int64

gcAnndata.obs['stage_sex'].value_counts()

female_E11.5    71
female_E16.5    71
male_E11.5      68
male_E16.5      67
male_E15.5      65
female_E12.5    48
male_E14.5      48
female_E14.5    48
female_E10.5    47
female_E15.5    47
male_E9.5       47
female_E9.5     46
male_E13.5      45
male_E12.5      40
female_E13.5    40
male_E10.5      24
Name: stage_sex, dtype: int64

gcAnndata.obs['sex'].head()

E10_GFP_pos_F1_10    female
E10_GFP_pos_F1_11    female
E10_GFP_pos_F1_12    female
E10_GFP_pos_F1_14    female
E10_GFP_pos_F1_15    female
Name: sex, dtype: object

gcAnndata.obs['CellID'] = gcAnndata.obs.index.tolist()

gcAnndata.var['Gene'] = gcAnndata.var.index.tolist()

sc.pl.violin(gcAnndata, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt'],
             jitter=0.4, multi_panel=True)

... storing 'sex' as categorical
... storing 'stage' as categorical
... storing 'Embryo' as categorical
... storing 'celltype' as categorical
... storing 'stage_sex' as categorical
... storing 'stage_sex_celltype' as categorical
... storing 'chromosome_name' as categorical
... storing 'external_gene_name' as categorical

sc.pp.filter_cells(gcAnndata, min_genes = 3500)
sc.pp.filter_cells(gcAnndata, max_genes = 10000)
sc.pp.filter_cells(gcAnndata, min_counts = 500000)
sc.pp.filter_genes(gcAnndata, min_cells = 5)
gcAnndata = gcAnndata[gcAnndata.obs['pct_counts_mt'] < 5, :]

filtered out 9596 genes that are detected in less than 5 cells

mito_genes = gcAnndata.var.chromosome_name.str.startswith('MT')
mito_genes

ENSMUSG00000051951    False
ENSMUSG00000102343    False
ENSMUSG00000025902    False
ENSMUSG00000033845    False
ENSMUSG00000025903    False
                      ...  
ENSMUSG00000065947     True
ENSMUSG00000064363     True
ENSMUSG00000064367     True
ENSMUSG00000064368     True
ENSMUSG00000064370     True
Name: chromosome_name, Length: 21838, dtype: bool

keep_genes = np.invert(mito_genes)

gcAnndata = gcAnndata[:,keep_genes]

# Retain same cells for allele-specific analyses keep = gcAnndata.obs.Sample #keep.to_csv(".../keep.csv")gcAnndata.layers["counts"] = gcAnndata[gcAnndata.obs_names, gcAnndata.var_names].X.copy()

gcAnndata.raw = gcAnndata

gcAnndata_germ = gcAnndata[gcAnndata.obs["celltype"] == "germ_cell"]
gcAnndata_som = gcAnndata[gcAnndata.obs["celltype"] == "somatic_cell"]

gcAnndata

AnnData object with n_obs × n_vars = 822 × 21827
    obs: 'Sample', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'sex', 'stage', 'Embryo', 'celltype', 'stage_sex', 'stage_sex_celltype', 'CellID', 'n_genes', 'n_counts'
    var: 'geneid', 'start_position', 'end_position', 'chromosome_name', 'external_gene_name', 'mt', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'Gene', 'n_cells'

sc.pl.violin(gcAnndata, ['n_genes_by_counts', 'total_counts', 'pct_counts_mt'],
             jitter=0.4, multi_panel=True)

plt.rcParams['figure.figsize'] = [5, 5]

sc.pp.normalize_total(gcAnndata, exclude_highly_expressed = True, max_fraction = 0.10, target_sum=1e4)

normalizing counts per cell The following highly-expressed genes are not considered during normalization factor computation:
['ENSMUSG00000052187', 'ENSMUSG00000055609', 'ENSMUSG00000069919']
    finished (0:00:00)

sc.pp.normalize_total(gcAnndata_germ, exclude_highly_expressed = True, max_fraction = 0.10, target_sum=1e4)

normalizing counts per cell The following highly-expressed genes are not considered during normalization factor computation:
[]
    finished (0:00:00)

sc.pp.log1p(gcAnndata)

sc.pp.log1p(gcAnndata_germ)

plt.rcParams['figure.figsize'] = [11.5, 2.5]
svm = sns.swarmplot(data=gcAnndata_germ.obs, x="stage", y="pct_counts_mt", hue="sex", dodge=True, s = 3, legend=True);
figure = svm.get_figure() 
#plt.savefig("/users/k2142342/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_13May24/Figures/pct_mito_germ.pdf", dpi = 300)

plt.rcParams['figure.figsize'] = [1.43, 2.5]
svm = sns.swarmplot(data=gcAnndata_som.obs, x="stage", y="pct_counts_mt", hue="sex", dodge=True, s = 3, legend=True);
figure = svm.get_figure() 
#plt.savefig("/users/k2142342/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_13May24/Figures/pct_mito_soma.pdf", dpi = 300)

plt.rcParams['figure.figsize'] = [11.5, 2.5]
svm = sns.swarmplot(data=gcAnndata_germ.obs, x="stage", y="n_genes_by_counts", hue="sex", dodge=True, s = 3, legend=True);
figure = svm.get_figure() 
#plt.savefig("/users/k2142342/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_13May24/Figures/n_genes_germ.pdf", dpi = 300)

plt.rcParams['figure.figsize'] = [1.43, 2.5]
svm = sns.swarmplot(data=gcAnndata_som.obs, x="stage", y="n_genes_by_counts", hue="sex", dodge=True, s = 3, legend=True);
figure = svm.get_figure() 
#plt.savefig("/users/k2142342/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_13May24/Figures/n_genes_soma.pdf", dpi = 300)

plt.rcParams['figure.figsize'] = [11.5, 2.5]
svm = sns.swarmplot(data=gcAnndata_germ.obs, x="stage", y="total_counts", hue="sex", dodge=True, s = 3, legend=True);
figure = svm.get_figure() 
#plt.savefig("/users/k2142342/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_13May24/Figures/total_counts_germ.pdf", dpi = 300)

plt.rcParams['figure.figsize'] = [1.43, 2.5]
svm = sns.swarmplot(data=gcAnndata_som.obs, x="stage", y="total_counts", hue="sex", dodge=True, s = 3, legend=True);
figure = svm.get_figure() 
#plt.savefig("/users/k2142342/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_13May24/Figures/total_counts_soma.pdf", dpi = 300)

plt.rcParams['figure.figsize'] = [4, 4]

sc.pp.highly_variable_genes(gcAnndata, flavor='seurat_v3', n_top_genes=800)

If you pass `n_top_genes`, all cutoffs are ignored.
extracting highly variable genes
--> added
    'highly_variable', boolean vector (adata.var)
    'highly_variable_rank', float vector (adata.var)
    'means', float vector (adata.var)
    'variances', float vector (adata.var)
    'variances_norm', float vector (adata.var)

sc.pp.highly_variable_genes(gcAnndata_germ, flavor='seurat_v3', n_top_genes=800)

If you pass `n_top_genes`, all cutoffs are ignored.
extracting highly variable genes
--> added
    'highly_variable', boolean vector (adata.var)
    'highly_variable_rank', float vector (adata.var)
    'means', float vector (adata.var)
    'variances', float vector (adata.var)
    'variances_norm', float vector (adata.var)

gcAnndata_germ.var['highly_variable'].value_counts()

False    21027
True       800
Name: highly_variable, dtype: int64

sc.pl.highly_variable_genes(gcAnndata_germ)

sc.tl.pca(gcAnndata_germ, svd_solver='arpack')

computing PCA
    on highly variable genes
    with n_comps=50
    finished (0:00:00)

sc.pl.pca(gcAnndata_germ, color=['stage_sex'], size=200, ec="black", linewidth=0.5, frameon=True)#

sc.pp.neighbors(gcAnndata, n_neighbors=5, n_pcs=50)

computing neighbors
WARNING: You’re trying to run this on 21827 dimensions of `.X`, if you really want this, set `use_rep='X'`.
         Falling back to preprocessing with `sc.pp.pca` and default params.
computing PCA
    with n_comps=50
    finished (0:00:00)
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:02)

sc.pp.neighbors(gcAnndata_germ, n_neighbors=5, n_pcs=50)

computing neighbors
    using 'X_pca' with n_pcs = 50
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:00)

sc.tl.leiden(gcAnndata_germ, resolution = 0.7, random_state = 55063) # Perform clustering

running Leiden clustering
    finished: found 11 clusters and added
    'leiden', the cluster labels (adata.obs, categorical) (0:00:00)

sc.tl.umap(gcAnndata_germ, random_state = 55063)

computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm) (0:00:00)

sc.pl.umap(gcAnndata_germ, color=['stage_sex'], size=100, ec="black", linewidth=0.5, frameon=True)

sc.tl.tsne(gcAnndata_germ, n_pcs=50, perplexity=41, learning_rate=1000, random_state = 55063)

computing tSNE
    using 'X_pca' with n_pcs = 50
    using sklearn.manifold.TSNE
    finished: added
    'X_tsne', tSNE coordinates (adata.obsm) (0:00:01)

#plt.rcParams['figure.figsize'] = [5,5]
sc.pl.tsne(gcAnndata_germ, color=['stage_sex'], size=150, ec="black", linewidth=0.5, frameon=True)#, save = '_stage_sex.png')

sc.pl.tsne(gcAnndata_germ, color=["leiden", 'stage_sex', 'Embryo'],legend_loc="on data", size=150, edgecolor="black", linewidths=0.5, frameon=True, legend_fontsize=8, legend_fontoutline=1)

sc.pl.tsne(gcAnndata_germ, color=['sex', 'stage', 'stage_sex'], size=150, ec="black", linewidth=0.5, frameon=True, show=False)#, save="_multi.png")

[<AxesSubplot: title={'center': 'sex'}, xlabel='tSNE1', ylabel='tSNE2'>,
 <AxesSubplot: title={'center': 'stage'}, xlabel='tSNE1', ylabel='tSNE2'>,
 <AxesSubplot: title={'center': 'stage_sex'}, xlabel='tSNE1', ylabel='tSNE2'>]

# colour scheme for nice plots
colours = ["#f1eef6", "#d8d6dd", "#d4b9da", "#c994c7", "#df65b0", "#e7298a", "#ce1256", "#91003f", 
           "#eff3ff", "#d7dae5", "#c6dbef", "#9ecae1", "#6baed6", "#4292c6", "#2171b5", "#084594"]


cats = ["male_E9.5", "male_E10.5", "male_E11.5", "male_E12.5", "male_E13.5", "male_E14.5", "male_E15.5", "male_E16.5",
       "female_E9.5", "female_E10.5", "female_E11.5", "female_E12.5", "female_E13.5", "female_E14.5", "female_E15.5", "female_E16.5"]

sc.pl.tsne(gcAnndata_germ, color = ['stage_sex'], palette = colours, size = 150, ec = "black", linewidth = 0.5, frameon = True)#, save = '_stage_sex.pdf')

gcAnndata_germ.obs.Sample = gcAnndata_germ.obs.Sample.astype('category')

sc.pl.pca(gcAnndata_germ, color='stage_sex', size=300, ec="black", frameon=True, legend_loc="on data", legend_fontsize=4, linewidth=0.2)

sc.pl.pca(gcAnndata_germ, color='stage_sex', size=800, ec="black", frameon=True, legend_fontsize=4, linewidth=0.2, projection='3d')

gcAnndata.obs

	Sample	n_genes_by_counts	total_counts	total_counts_mt	pct_counts_mt	sex	stage	Embryo	celltype	stage_sex	stage_sex_celltype	CellID	n_genes	n_counts
E10_GFP_pos_F1_10	E10_GFP_pos_F1_10	8707	715292	918	0.128339	female	E10.5	F1	germ_cell	female_E10.5	female_E10.5_germ_cell	E10_GFP_pos_F1_10	8707	715292
E10_GFP_pos_F1_11	E10_GFP_pos_F1_11	8473	1564689	5792	0.370169	female	E10.5	F1	germ_cell	female_E10.5	female_E10.5_germ_cell	E10_GFP_pos_F1_11	8473	1564689
E10_GFP_pos_F1_12	E10_GFP_pos_F1_12	8206	2310070	3992	0.172809	female	E10.5	F1	germ_cell	female_E10.5	female_E10.5_germ_cell	E10_GFP_pos_F1_12	8206	2310070
E10_GFP_pos_F1_14	E10_GFP_pos_F1_14	8338	1320374	5065	0.383603	female	E10.5	F1	germ_cell	female_E10.5	female_E10.5_germ_cell	E10_GFP_pos_F1_14	8338	1320374
E10_GFP_pos_F1_15	E10_GFP_pos_F1_15	8473	1283668	3195	0.248896	female	E10.5	F1	germ_cell	female_E10.5	female_E10.5_germ_cell	E10_GFP_pos_F1_15	8473	1283668
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
M4_GFPP_P2_B5	M4_GFPP_P2_B5	5349	1735796	15873	0.914451	male	E12.5	M4	germ_cell	male_E12.5	male_E12.5_germ_cell	M4_GFPP_P2_B5	5349	1735796
M4_GFPP_P2_B7	M4_GFPP_P2_B7	6595	2647587	22935	0.866260	male	E12.5	M4	germ_cell	male_E12.5	male_E12.5_germ_cell	M4_GFPP_P2_B7	6595	2647587
M4_GFPP_P2_B8	M4_GFPP_P2_B8	5555	1920124	20754	1.080868	male	E12.5	M4	germ_cell	male_E12.5	male_E12.5_germ_cell	M4_GFPP_P2_B8	5555	1920124
M4_GFPP_P2_B9	M4_GFPP_P2_B9	5391	1287828	9147	0.710266	male	E12.5	M4	germ_cell	male_E12.5	male_E12.5_germ_cell	M4_GFPP_P2_B9	5391	1287828
M4_GFPP_P2_D2	M4_GFPP_P2_D2	6042	3026218	22180	0.732928	male	E12.5	M4	germ_cell	male_E12.5	male_E12.5_germ_cell	M4_GFPP_P2_D2	6042	3026218

822 rows × 14 columns

gcAnndata.obs.Sample = gcAnndata.obs.Sample.astype('category')

sc.tl.dendrogram(gcAnndata, groupby='stage_sex_celltype', optimal_ordering = 'True')

    using 'X_pca' with n_pcs = 50
Storing dendrogram info using `.uns['dendrogram_stage_sex_celltype']`

sc.pl.correlation_matrix(gcAnndata, groupby="stage_sex_celltype", figsize=[5,4])

gcAnndata_f = gcAnndata_germ[gcAnndata_germ.obs["sex"] == 'female']

gcAnndata_germ[:,gcAnndata_germ.var[gcAnndata_germ.var.chromosome_name == 'X'].geneid]

View of AnnData object with n_obs × n_vars = 774 × 755
    obs: 'Sample', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'sex', 'stage', 'Embryo', 'celltype', 'stage_sex', 'stage_sex_celltype', 'CellID', 'n_genes', 'n_counts', 'leiden'
    var: 'geneid', 'start_position', 'end_position', 'chromosome_name', 'external_gene_name', 'mt', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'Gene', 'n_cells', 'highly_variable', 'highly_variable_rank', 'means', 'variances', 'variances_norm'
    uns: 'log1p', 'hvg', 'pca', 'stage_sex_colors', 'neighbors', 'leiden', 'umap', 'tsne', 'leiden_colors', 'Embryo_colors', 'sex_colors', 'stage_colors'
    obsm: 'X_pca', 'X_umap', 'X_tsne'
    varm: 'PCs'
    obsp: 'distances', 'connectivities'

Xgenes = gcAnndata_germ.var.index[gcAnndata_germ.var.chromosome_name=="X"].to_list()
C9genes = gcAnndata_germ.var.index[gcAnndata_germ.var.chromosome_name=="9"].to_list()
Agenes = gcAnndata_germ.var.index[~gcAnndata_germ.var.chromosome_name.isin(['X', 'Y', 'mt'])].to_list()

len(Xgenes)

755

gcAnndata_germ.obs["Chr X"] = (np.mean(gcAnndata_germ[:, Xgenes].X, axis=1) / np.mean(gcAnndata_germ[:, Agenes].X, axis=1)).tolist()

gcAnndata_germ.obs["Chr 9"] = (np.mean(gcAnndata_germ[:, C9genes].X, axis=1) / np.mean(gcAnndata_germ[:, Agenes].X, axis=1)).tolist()

germXA_data = gcAnndata_germ.obs["stage_sex"].to_frame().join(gcAnndata_germ.obs["Chr 9"]).join(gcAnndata_germ.obs["Chr X"])
#Melt the data to long format
germXA_data_melted = germXA_data.melt(id_vars="stage_sex", var_name="key", value_name="value")

germXA_data_melted.groupby(['stage_sex', 'key']).describe()

		value
		count	mean	std	min	25%	50%	75%	max
stage_sex	key
female_E9.5	Chr 9	46.0	1.039709	0.025462	0.984258	1.024952	1.038626	1.057714	1.084630
	Chr X	46.0	0.803227	0.068857	0.661499	0.759838	0.796646	0.848185	1.011548
female_E10.5	Chr 9	47.0	1.018178	0.026375	0.964124	0.996394	1.016021	1.039820	1.083480
	Chr X	47.0	0.827360	0.071003	0.642227	0.786770	0.829078	0.882635	0.969546
female_E11.5	Chr 9	47.0	1.034468	0.026669	0.980696	1.017420	1.031443	1.048950	1.105648
	Chr X	47.0	0.850550	0.065388	0.656015	0.804692	0.866161	0.893809	0.980085
female_E12.5	Chr 9	48.0	1.004120	0.046151	0.900822	0.978298	1.004921	1.022318	1.176105
	Chr X	48.0	0.894220	0.093885	0.675559	0.832109	0.890431	0.965558	1.110798
female_E13.5	Chr 9	40.0	1.050803	0.044856	0.935329	1.027651	1.061018	1.080815	1.128320
	Chr X	40.0	0.998158	0.121724	0.738714	0.915549	0.983358	1.086108	1.403641
female_E14.5	Chr 9	48.0	1.057175	0.051442	0.948005	1.020301	1.059237	1.081211	1.186694
	Chr X	48.0	1.068013	0.122933	0.877402	0.975222	1.056255	1.146100	1.383988
female_E15.5	Chr 9	47.0	1.043672	0.040359	0.948302	1.016188	1.047597	1.067518	1.142354
	Chr X	47.0	1.040285	0.102518	0.817350	0.978810	1.031370	1.088123	1.360234
female_E16.5	Chr 9	71.0	1.045216	0.046085	0.915336	1.023229	1.048614	1.067764	1.255449
	Chr X	71.0	1.046350	0.111389	0.757019	0.974011	1.050920	1.131289	1.263218
male_E9.5	Chr 9	47.0	1.037535	0.027919	0.984775	1.018983	1.034444	1.056644	1.116604
	Chr X	47.0	0.777054	0.059360	0.660179	0.745140	0.777877	0.799905	0.986266
male_E10.5	Chr 9	24.0	1.046189	0.063459	0.977798	1.008437	1.027262	1.050145	1.227075
	Chr X	24.0	0.778304	0.080767	0.598791	0.741982	0.780417	0.833423	0.912178
male_E11.5	Chr 9	44.0	1.020873	0.033534	0.897119	1.004651	1.021623	1.040349	1.107649
	Chr X	44.0	0.764596	0.052674	0.665379	0.733369	0.753785	0.806336	0.926515
male_E12.5	Chr 9	40.0	1.007538	0.035555	0.940206	0.987942	1.007853	1.031149	1.090051
	Chr X	40.0	0.695224	0.046428	0.594265	0.670286	0.691706	0.724281	0.824372
male_E13.5	Chr 9	45.0	1.003224	0.042403	0.892110	0.981161	1.004231	1.035544	1.085701
	Chr X	45.0	0.715640	0.078493	0.525082	0.683332	0.711432	0.770313	0.852518
male_E14.5	Chr 9	48.0	1.029505	0.027309	0.983100	1.007506	1.026058	1.048771	1.091244
	Chr X	48.0	0.789000	0.060126	0.655340	0.749864	0.796295	0.826562	0.944256
male_E15.5	Chr 9	65.0	1.009524	0.031620	0.941515	0.987887	1.009326	1.030966	1.091572
	Chr X	65.0	0.781767	0.069406	0.616910	0.744473	0.771530	0.814544	1.122267
male_E16.5	Chr 9	67.0	1.039657	0.038145	0.960685	1.014215	1.038578	1.061313	1.197431
	Chr X	67.0	0.663442	0.048090	0.532843	0.633841	0.661075	0.692989	0.776138

plt.rcParams["figure.figsize"]=4,4
bx = sns.violinplot(x="stage_sex", y="value",
            hue="key", palette=["#0D8DB3","#C70039"],fliersize=0.1,linewidth=0.5,split=True,scale="count",inner="quartile",
            data=germXA_data_melted)
bx.set(xlabel="")
bx.set_ylabel("Chromosome:Autosome ratio", fontsize=8)
bx.set_xticklabels(bx.get_xticklabels(), rotation=40, fontsize=8,horizontalalignment="right", rotation_mode="anchor", visible=True)
bx.grid(linestyle='-', linewidth='0.1')
bx.tick_params(
    axis="both",          # changes apply to the x-axis
    which="major",      # both major and minor ticks are affected
    bottom=True,      # ticks along the bottom edge are off
    top=False,  left=True, labeltop=False,      # ticks along the top edge are off
    labelbottom=True, pad=0.5, length=1.5)
bx.axhline(y=0.5,color='gray',linestyle='--')
bx.axhline(y=1,color='gray',linestyle='--')
plt.legend(loc="upper right", fontsize=6, frameon=False)
plt.tight_layout()

#figure = svm.get_figure()    
#plt.savefig('.../X_Aratios.pdf', dpi=600)

Stats

from scipy import stats

stats.mannwhitneyu(germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('male') & germXA_data_melted['key'].isin(['Chr X'])].value,
              germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('female') & germXA_data_melted['key'].isin(['Chr X'])].value)

MannwhitneyuResult(statistic=91274.0, pvalue=2.937609771698074e-29)

stats.mannwhitneyu(germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('male') & germXA_data_melted['key'].isin(['Chr 9'])].value,
              germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('female') & germXA_data_melted['key'].isin(['Chr 9'])].value)

MannwhitneyuResult(statistic=137361.0, pvalue=0.005546971119452869)

# plt.rcParams["figure.figsize"]=10,3
#p = sns.scatterplot(gcAnndata.obs.Sample, gcAnndata.obs['Chr X'], hue=gcAnndata.obs.sex, legend=False)
# plt.setp(p.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor");

stats.mannwhitneyu(germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('female_E14.5') & germXA_data_melted['key'].isin(['Chr X'])].value,
              germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('male_E14.5') & germXA_data_melted['key'].isin(['Chr X'])].value)
# At E14.5, female X output is significantly different to E14.5 male

MannwhitneyuResult(statistic=3446.0, pvalue=1.3128901010109332e-06)

stats.mannwhitneyu(germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('female_E14.5') & germXA_data_melted['key'].isin(['Chr 9'])].value,
              germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('male_E14.5') & germXA_data_melted['key'].isin(['Chr 9'])].value)
# At E14.5, female 9 output is NOT significantly different to E14.5 male

MannwhitneyuResult(statistic=2738.0, pvalue=0.06617788001630687)

stats.mannwhitneyu(germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('female_E14.5') & germXA_data_melted['key'].isin(['Chr X'])].value,
              germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('female_E14.5') & germXA_data_melted['key'].isin(['Chr 9'])].value)
# At E14.5, female X output is NOT significantly different to E14.5 female 9 output

MannwhitneyuResult(statistic=1126.0, pvalue=0.8517746681220008)

stats.mannwhitneyu(germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('male_E14.5') & germXA_data_melted['key'].isin(['Chr X'])].value,
              germXA_data_melted[germXA_data_melted['stage_sex'].str.contains('male_E14.5') & germXA_data_melted['key'].isin(['Chr 9'])].value)
# At E14.5, male X output is significantly different to E14.5 male 9 output

MannwhitneyuResult(statistic=2453.0, pvalue=2.1919850389139528e-08)

PAGA

sc.tl.paga(gcAnndata_germ, groups="stage_sex")
#sc.tl.paga(gcAnndata_germ, groups="stage_sex_celltype")

running PAGA
    finished: added
    'paga/connectivities', connectivities adjacency (adata.uns)
    'paga/connectivities_tree', connectivities subtree (adata.uns) (0:00:00)

sc.pl.paga(gcAnndata_germ, threshold = 0.1, labels=None)
sc.pl.paga(gcAnndata_germ,
           threshold = 0.2565,
           color = "stage_sex",
           fontsize=4,
           fontoutline=1)#, 
           #layout='fa',
           #save='_PAGA.pdf')

--> added 'pos', the PAGA positions (adata.uns['paga'])

--> added 'pos', the PAGA positions (adata.uns['paga'])

gcAnndata_germ.uns['iroot'] = np.flatnonzero(gcAnndata_germ.obs["stage_sex"]  == "male_E9.5")[0]

sc.tl.dpt(gcAnndata_germ, 
          n_branchings = 1)

WARNING: Trying to run `tl.dpt` without prior call of `tl.diffmap`. Falling back to `tl.diffmap` with default parameters.
computing Diffusion Maps using n_comps=15(=n_dcs)
computing transitions
    finished (0:00:00)
    eigenvalues of transition matrix
    [1.         0.9991412  0.9982408  0.9951082  0.9941651  0.9870595
     0.9859078  0.97948915 0.9767556  0.9663165  0.96494716 0.9602786
     0.9577897  0.9386192  0.9367913 ]
    finished: added
    'X_diffmap', diffmap coordinates (adata.obsm)
    'diffmap_evals', eigenvalues of transition matrix (adata.uns) (0:00:00)
computing Diffusion Pseudotime using n_dcs=10
    finished: added
    'dpt_pseudotime', the pseudotime (adata.obs)
    'dpt_groups', the branching subgroups of dpt (adata.obs)
    'dpt_order', cell order (adata.obs) (0:00:00)

sc.pl.tsne(gcAnndata_germ, color=["dpt_pseudotime", "stage_sex"], size=200,frameon=True, ec="black", linewidth=0.4)
sc.pl.tsne(gcAnndata_germ, color=["dpt_pseudotime"], size=200,frameon=True, ec="black", linewidth=0.4)#, save = '_pseudotime.pdf')

DE gene and marker analysis

sc.pl.matrixplot(gcAnndata_germ, var_names=["Eif2s3y", "Ddx3y", "Uty", "Usp9y", "Uba1y", "Kdm5d", "Sly", "Zfy2"], 
                 gene_symbols="external_gene_name",
                 cmap=heatcolors_wr, 
                 use_raw=False,
                 groupby="stage_sex", 
                 #dendrogram = True,
                 swap_axes = True,
                 standard_scale="var"),
                 #save = '.../_Ygenes.png')

(None,)

#gcAnndata_germ.var
sc.pl.tsne(gcAnndata_germ, 
           color=["Tfap2c", "Prdm14", "Pou5f1", "Nanog", "Dnmt3a", "Dnmt3l", "Dnmt3b", "Dnmt3c", "Tet1", "Tet2", "Lefty1", "Lefty2", "Etv1",
                  "Pitx2", "Hesx1", "Egr4", "Phlda2", "Gata2", "Rec8", "Stra8", "Prdm9", "Spo11", "Atr", "Atm"], 
           gene_symbols = 'external_gene_name', 
           use_raw=False, 
           legend_fontsize=6, 
           size=60, 
           edgecolor="black", 
           linewidths=0.1,
           wspace=0.05, 
           hspace=0.2, 
           cmap=heatcolors, 
           ncols=7)#,
           #save="_male_female_candidategenes")

#gcAnndata_germ.var
sc.pl.tracksplot(gcAnndata_germ, var_names=["Tfap2c", "Prdm14", "Pou5f1", "Nanog", 
                                       "Dnmt3a", "Dnmt3l", "Dnmt3b", "Dnmt3c", "Tet1", "Tet2", 
                                       "Lefty1", "Lefty2", "Etv1", "Pitx2", "Hesx1", "Egr4", 
                                       "Phlda2", "Gata2", "Rec8", "Stra8", "Prdm9", "Spo11", 
                                       "Atr", "Atm", "Msh6", "Aurka"], 
                 gene_symbols = 'external_gene_name', 
                 use_raw=False, 
                 dendrogram=False,
                 groupby='stage_sex',
                 #save = "tracksplot_malefemale.pdf",
                 )

%%R -o Ezh2_derep_X
Ezh2_derep_X <- read.table("~/Lab_Sangrithi/working/Mahesh_Sangrithi/CutNRun/QMUL_13Oct23/deepTools_OUT/H3K27me3_Female/DE_genes_in_Ezh2_dataset_chrX.txt", sep = "\t", header = F)

colnames(Ezh2_derep_X) =  colnames = c("chromosome", "start", "end", "ensembl_gene_id", "external_gene_name")

sc.pl.matrixplot(gcAnndata_f, 
                 var_names = gcAnndata_f.var[gcAnndata_f.var.geneid.isin(Ezh2_derep_X.ensembl_gene_id)].external_gene_name,
                 gene_symbols="external_gene_name",
                 cmap=heatcolors_wr, 
                 groupby="stage_sex", 
                 #dendrogram = True,
                 swap_axes = True,
                 standard_scale="var",
                 figsize = [3,8])#, save="_Ezh2_derep_X.pdf")

sc.tl.rank_genes_groups(gcAnndata, groupby="stage_sex_celltype", 
                        groups=["female_E11.5_somatic_cell"], 
                        reference = "male_E11.5_somatic_cell", 
                        method='wilcoxon', 
                        use_raw = False,
                        tie_correct=True, 
                        key_added="germ_soma", 
                        pts=True
                       )

ranking genes
--> Few observations in a group for normal approximation (<=25). Lower test accuracy.
    finished: added to `.uns['germ_soma']`
    'names', sorted np.recarray to be indexed by group ids
    'scores', sorted np.recarray to be indexed by group ids
    'logfoldchanges', sorted np.recarray to be indexed by group ids
    'pvals', sorted np.recarray to be indexed by group ids
    'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)

germ_soma = sc.get.rank_genes_groups_df(gcAnndata, group="female_E11.5_somatic_cell", key="germ_soma", log2fc_min = 2, pval_cutoff=0.05)
germ_soma
# this is Xist

	names	scores	logfoldchanges	pvals	pvals_adj	pct_nz_group
0	ENSMUSG00000086503	6.074503	6.09966	1.243720e-09	0.000014	1.0

sc.tl.rank_genes_groups(gcAnndata_germ, 
                        use_raw=False,
                        groupby="stage_sex", 
                        groups=["female_E16.5", "female_E15.5", "female_E14.5", "female_E13.5", "female_E12.5", "female_E11.5", "female_E10.5", "female_E9.5"], 
                        reference = 'rest', 
                        method='wilcoxon', 
                        tie_correct=True, 
                        key_added="female", 
                        pts=True)

ranking genes
    finished: added to `.uns['female']`
    'names', sorted np.recarray to be indexed by group ids
    'scores', sorted np.recarray to be indexed by group ids
    'logfoldchanges', sorted np.recarray to be indexed by group ids
    'pvals', sorted np.recarray to be indexed by group ids
    'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:07)

sc.tl.rank_genes_groups(gcAnndata_germ, 
                        groupby="stage_sex", 
                        groups=["male_E16.5", "male_E15.5", "male_E14.5", "male_E13.5", "male_E12.5", "male_E11.5", "male_E10.5", "male_E9.5"], 
                        reference = 'rest', 
                        method='wilcoxon', 
                        tie_correct=True, 
                        key_added="male", 
                        pts=True)

ranking genes
WARNING: It seems you use rank_genes_groups on the raw count data. Please logarithmize your data before calling rank_genes_groups.
    finished: added to `.uns['male']`
    'names', sorted np.recarray to be indexed by group ids
    'scores', sorted np.recarray to be indexed by group ids
    'logfoldchanges', sorted np.recarray to be indexed by group ids
    'pvals', sorted np.recarray to be indexed by group ids
    'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:08)

## Male E16.5
sc.tl.rank_genes_groups(gcAnndata_germ, groupby="stage_sex", groups=["male_E16.5"], reference = "male_E15.5", method='wilcoxon', tie_correct=True, key_added="male", pts=True, use_raw=False)
m_df_E16_5 = sc.get.rank_genes_groups_df(gcAnndata_germ, group="male_E16.5", key="male", pval_cutoff=0.01, log2fc_min = 1.5)
m_df_E16_5[1:20]
## Male E15.5
sc.tl.rank_genes_groups(gcAnndata_germ, groupby="stage_sex", groups=["male_E15.5"], reference = "male_E14.5", method='wilcoxon', tie_correct=True, key_added="male", pts=True, use_raw=False)
m_df_E15_5 = sc.get.rank_genes_groups_df(gcAnndata_germ, group="male_E15.5", key="male", pval_cutoff=0.01, log2fc_min = 1.5)
m_df_E15_5[1:20]
## Male E14.5
sc.tl.rank_genes_groups(gcAnndata_germ, groupby="stage_sex", groups=["male_E14.5"], reference = "male_E13.5", method='wilcoxon', tie_correct=True, key_added="male", pts=True, use_raw=False)
m_df_E14_5 = sc.get.rank_genes_groups_df(gcAnndata_germ, group="male_E14.5", key="male", pval_cutoff=0.01, log2fc_min = 1.5)
m_df_E14_5[1:20]
## Male E13.5
sc.tl.rank_genes_groups(gcAnndata_germ, groupby="stage_sex", groups=["male_E13.5"], reference = "male_E12.5", method='wilcoxon', tie_correct=True, key_added="male", pts=True, use_raw=False)
m_df_E13_5 = sc.get.rank_genes_groups_df(gcAnndata_germ, group="male_E13.5", key="male", pval_cutoff=0.01, log2fc_min = 1.5)
m_df_E13_5[1:20]
## Male E12.5 
sc.tl.rank_genes_groups(gcAnndata_germ, groupby="stage_sex", groups=["male_E12.5"], reference = "male_E11.5", method='wilcoxon', tie_correct=True, key_added="male", pts=True, use_raw=False)
m_df_E12_5 = sc.get.rank_genes_groups_df(gcAnndata_germ, group="male_E12.5", key="male", pval_cutoff=0.01, log2fc_min = 1.5)
m_df_E12_5[1:20]
## Male E11.5 
sc.tl.rank_genes_groups(gcAnndata_germ, groupby="stage_sex", groups=["male_E11.5"], reference = "male_E10.5", method='wilcoxon', tie_correct=True, key_added="male", pts=True, use_raw=False)
m_df_E11_5 = sc.get.rank_genes_groups_df(gcAnndata_germ, group="male_E11.5", key="male", pval_cutoff=0.01, log2fc_min = 1.5)
m_df_E11_5[1:20]
## Male E10.5 
sc.tl.rank_genes_groups(gcAnndata_germ, groupby="stage_sex", groups=["male_E10.5"], reference = "male_E9.5", method='wilcoxon', tie_correct=True, key_added="male", pts=True, use_raw=False)
m_df_E10_5 = sc.get.rank_genes_groups_df(gcAnndata_germ, group="male_E10.5", key="male", pval_cutoff=0.01, log2fc_min = 1.5)
m_df_E10_5[1:20]

ranking genes
    finished: added to `.uns['male']`
    'names', sorted np.recarray to be indexed by group ids
    'scores', sorted np.recarray to be indexed by group ids
    'logfoldchanges', sorted np.recarray to be indexed by group ids
    'pvals', sorted np.recarray to be indexed by group ids
    'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
    finished: added to `.uns['male']`
    'names', sorted np.recarray to be indexed by group ids
    'scores', sorted np.recarray to be indexed by group ids
    'logfoldchanges', sorted np.recarray to be indexed by group ids
    'pvals', sorted np.recarray to be indexed by group ids
    'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
    finished: added to `.uns['male']`
    'names', sorted np.recarray to be indexed by group ids
    'scores', sorted np.recarray to be indexed by group ids
    'logfoldchanges', sorted np.recarray to be indexed by group ids
    'pvals', sorted np.recarray to be indexed by group ids
    'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
    finished: added to `.uns['male']`
    'names', sorted np.recarray to be indexed by group ids
    'scores', sorted np.recarray to be indexed by group ids
    'logfoldchanges', sorted np.recarray to be indexed by group ids
    'pvals', sorted np.recarray to be indexed by group ids
    'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
    finished: added to `.uns['male']`
    'names', sorted np.recarray to be indexed by group ids
    'scores', sorted np.recarray to be indexed by group ids
    'logfoldchanges', sorted np.recarray to be indexed by group ids
    'pvals', sorted np.recarray to be indexed by group ids
    'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
--> Few observations in a group for normal approximation (<=25). Lower test accuracy.
    finished: added to `.uns['male']`
    'names', sorted np.recarray to be indexed by group ids
    'scores', sorted np.recarray to be indexed by group ids
    'logfoldchanges', sorted np.recarray to be indexed by group ids
    'pvals', sorted np.recarray to be indexed by group ids
    'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
--> Few observations in a group for normal approximation (<=25). Lower test accuracy.
    finished: added to `.uns['male']`
    'names', sorted np.recarray to be indexed by group ids
    'scores', sorted np.recarray to be indexed by group ids
    'logfoldchanges', sorted np.recarray to be indexed by group ids
    'pvals', sorted np.recarray to be indexed by group ids
    'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)

	names	scores	logfoldchanges	pvals	pvals_adj	pct_nz_group
1	ENSMUSG00000053332	6.089863	1.514426	1.130071e-09	5.138761e-07	1.000000
2	ENSMUSG00000040459	5.943998	2.714579	2.781523e-09	8.442897e-07	1.000000
3	ENSMUSG00000074656	5.919688	1.746964	3.225538e-09	9.026130e-07	1.000000
4	ENSMUSG00000071072	5.725201	1.613739	1.033114e-08	1.533999e-06	1.000000
5	ENSMUSG00000090733	5.713046	1.502842	1.109720e-08	1.562700e-06	1.000000
6	ENSMUSG00000114430	5.518103	8.323348	3.426794e-08	2.991866e-06	0.583333
7	ENSMUSG00000009927	5.482092	1.591199	4.203241e-08	3.410563e-06	1.000000
8	ENSMUSG00000057278	5.469937	1.505291	4.501951e-08	3.534679e-06	1.000000
9	ENSMUSG00000060475	5.433471	1.959378	5.526829e-08	3.981323e-06	1.000000
10	ENSMUSG00000038486	5.250492	24.453619	1.516937e-07	8.115243e-06	0.500000
11	ENSMUSG00000037958	5.056823	2.336012	4.262994e-07	1.678038e-05	1.000000
12	ENSMUSG00000029836	4.995876	1.754870	5.856930e-07	2.102619e-05	0.958333
13	ENSMUSG00000066800	4.989806	23.183596	6.044012e-07	2.155599e-05	0.458333
14	ENSMUSG00000075307	4.989806	26.324383	6.044012e-07	2.155599e-05	0.458333
15	ENSMUSG00000022982	4.971565	1.552454	6.641455e-07	2.297354e-05	1.000000
16	ENSMUSG00000014504	4.959410	1.902372	7.070770e-07	2.414575e-05	1.000000
17	ENSMUSG00000078784	4.947254	2.059873	7.526762e-07	2.508193e-05	1.000000
18	ENSMUSG00000039221	4.935099	1.609030	8.011016e-07	2.633380e-05	1.000000
19	ENSMUSG00000113902	4.922944	1.954452	8.525209e-07	2.752659e-05	1.000000

## Female E16.5
sc.tl.rank_genes_groups(gcAnndata_germ, groupby="stage_sex", groups=["female_E16.5"], reference = "female_E15.5", method='wilcoxon', tie_correct=True, key_added="female", pts=True, use_raw=False)
f_df_E16_5 = sc.get.rank_genes_groups_df(gcAnndata_germ, group="female_E16.5", key="female", pval_cutoff=0.01, log2fc_min = 1.5)
f_df_E16_5[1:20]
## Female E15.5
sc.tl.rank_genes_groups(gcAnndata_germ, groupby="stage_sex", groups=["female_E15.5"], reference = "female_E14.5", method='wilcoxon', tie_correct=True, key_added="female", pts=True, use_raw=False)
f_df_E15_5 = sc.get.rank_genes_groups_df(gcAnndata_germ, group="female_E15.5", key="female", pval_cutoff=0.01, log2fc_min = 1.5)
f_df_E15_5[1:20]
## Female E14.5
sc.tl.rank_genes_groups(gcAnndata_germ, groupby="stage_sex", groups=["female_E14.5"], reference = "female_E13.5", method='wilcoxon', tie_correct=True, key_added="female", pts=True, use_raw=False)
f_df_E14_5 = sc.get.rank_genes_groups_df(gcAnndata_germ, group="female_E14.5", key="female", pval_cutoff=0.01, log2fc_min = 1.5)
f_df_E14_5[1:20]
## Female E13.5
sc.tl.rank_genes_groups(gcAnndata_germ, groupby="stage_sex", groups=["female_E13.5"], reference = "female_E12.5", method='wilcoxon', tie_correct=True, key_added="female", pts=True, use_raw=False)
f_df_E13_5 = sc.get.rank_genes_groups_df(gcAnndata_germ, group="female_E13.5", key="female", pval_cutoff=0.01, log2fc_min = 1.5)
f_df_E13_5[1:20]
## Female E12.5 
sc.tl.rank_genes_groups(gcAnndata_germ, groupby="stage_sex", groups=["female_E12.5"], reference = "female_E11.5", method='wilcoxon', tie_correct=True, key_added="female", pts=True, use_raw=False)
f_df_E12_5 = sc.get.rank_genes_groups_df(gcAnndata_germ, group="female_E12.5", key="female", pval_cutoff=0.01, log2fc_min = 1.5)
f_df_E12_5[1:20]
## Female E11.5 
sc.tl.rank_genes_groups(gcAnndata_germ, groupby="stage_sex", groups=["female_E11.5"], reference = "female_E10.5", method='wilcoxon', tie_correct=True, key_added="female", pts=True, use_raw=False)
f_df_E11_5 = sc.get.rank_genes_groups_df(gcAnndata_germ, group="female_E11.5", key="female", pval_cutoff=0.01, log2fc_min = 1.5)
f_df_E11_5[1:20]
## Male E11.5 
sc.tl.rank_genes_groups(gcAnndata_germ, groupby="stage_sex", groups=["female_E10.5"], reference = "female_E9.5", method='wilcoxon', tie_correct=True, key_added="female", pts=True, use_raw=False)
f_df_E10_5 = sc.get.rank_genes_groups_df(gcAnndata_germ, group="female_E10.5", key="female", pval_cutoff=0.01, log2fc_min = 1.5)
f_df_E10_5[1:20]

ranking genes
    finished: added to `.uns['female']`
    'names', sorted np.recarray to be indexed by group ids
    'scores', sorted np.recarray to be indexed by group ids
    'logfoldchanges', sorted np.recarray to be indexed by group ids
    'pvals', sorted np.recarray to be indexed by group ids
    'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
    finished: added to `.uns['female']`
    'names', sorted np.recarray to be indexed by group ids
    'scores', sorted np.recarray to be indexed by group ids
    'logfoldchanges', sorted np.recarray to be indexed by group ids
    'pvals', sorted np.recarray to be indexed by group ids
    'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
    finished: added to `.uns['female']`
    'names', sorted np.recarray to be indexed by group ids
    'scores', sorted np.recarray to be indexed by group ids
    'logfoldchanges', sorted np.recarray to be indexed by group ids
    'pvals', sorted np.recarray to be indexed by group ids
    'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
    finished: added to `.uns['female']`
    'names', sorted np.recarray to be indexed by group ids
    'scores', sorted np.recarray to be indexed by group ids
    'logfoldchanges', sorted np.recarray to be indexed by group ids
    'pvals', sorted np.recarray to be indexed by group ids
    'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
    finished: added to `.uns['female']`
    'names', sorted np.recarray to be indexed by group ids
    'scores', sorted np.recarray to be indexed by group ids
    'logfoldchanges', sorted np.recarray to be indexed by group ids
    'pvals', sorted np.recarray to be indexed by group ids
    'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
    finished: added to `.uns['female']`
    'names', sorted np.recarray to be indexed by group ids
    'scores', sorted np.recarray to be indexed by group ids
    'logfoldchanges', sorted np.recarray to be indexed by group ids
    'pvals', sorted np.recarray to be indexed by group ids
    'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)
ranking genes
    finished: added to `.uns['female']`
    'names', sorted np.recarray to be indexed by group ids
    'scores', sorted np.recarray to be indexed by group ids
    'logfoldchanges', sorted np.recarray to be indexed by group ids
    'pvals', sorted np.recarray to be indexed by group ids
    'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)

	names	scores	logfoldchanges	pvals	pvals_adj	pct_nz_group
1	ENSMUSG00000020180	7.369151	1.679681	1.717180e-13	7.496178e-10	1.000000
2	ENSMUSG00000022114	7.107888	2.564145	1.178320e-12	2.857688e-09	1.000000
3	ENSMUSG00000069309	6.657501	3.684765	2.785229e-11	1.676702e-08	1.000000
4	ENSMUSG00000082766	6.527179	7.879493	6.702008e-11	2.985402e-08	0.680851
5	ENSMUSG00000035851	6.339468	1.651302	2.305602e-10	7.293388e-08	1.000000
6	ENSMUSG00000026878	6.316509	1.591184	2.675371e-10	8.342190e-08	0.978723
7	ENSMUSG00000091625	6.285678	1.743592	3.264251e-10	9.374844e-08	1.000000
8	ENSMUSG00000078784	6.185784	1.546489	6.179468e-10	1.466079e-07	0.978723
9	ENSMUSG00000038486	5.951748	6.214230	2.652936e-09	4.420278e-07	0.617021
10	ENSMUSG00000052748	5.855800	1.717659	4.747197e-09	6.452784e-07	0.978723
11	ENSMUSG00000014504	5.855363	1.563164	4.759693e-09	6.452784e-07	0.978723
12	ENSMUSG00000000365	5.830296	1.857088	5.532935e-09	7.188535e-07	0.978723
13	ENSMUSG00000032643	5.614338	2.537704	1.973158e-08	1.897274e-06	0.957447
14	ENSMUSG00000050199	5.513436	1.835604	3.518940e-08	3.012075e-06	0.978723
15	ENSMUSG00000114430	5.459807	7.603449	4.766512e-08	3.755908e-06	0.595745
16	ENSMUSG00000022781	5.383487	1.618103	7.305638e-08	5.351012e-06	0.978723
17	ENSMUSG00000063253	5.288535	1.935931	1.233001e-07	8.332108e-06	0.936170
18	ENSMUSG00000022197	5.164373	3.494497	2.412458e-07	1.379182e-05	0.723404
19	ENSMUSG00000039209	5.002163	2.637339	5.669061e-07	2.616038e-05	0.744681

Male v Female gslist

sc.tl.rank_genes_groups(gcAnndata, 'stage_sex', method='wilcoxon', use_raw=False)

ranking genes
    finished: added to `.uns['rank_genes_groups']`
    'names', sorted np.recarray to be indexed by group ids
    'scores', sorted np.recarray to be indexed by group ids
    'logfoldchanges', sorted np.recarray to be indexed by group ids
    'pvals', sorted np.recarray to be indexed by group ids
    'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:01)

sc.pl.rank_genes_groups(gcAnndata, n_genes=25, sharey=False, gene_symbols="external_gene_name")

result = gcAnndata.uns['rank_genes_groups']
groups = result['names'].dtype.names
pd.DataFrame(
    {group + '_' + key[:1]: result[key][group]
    for group in groups for key in ['names', 'scores', 'logfoldchanges', 'pvals_adj']}).head(50)

	female_E9.5_n	female_E9.5_s	female_E9.5_l	female_E9.5_p	female_E10.5_n	female_E10.5_s	female_E10.5_l	female_E10.5_p	female_E11.5_n	female_E11.5_s	female_E11.5_l	female_E11.5_p	female_E12.5_n	female_E12.5_s	female_E12.5_l	female_E12.5_p	female_E13.5_n	female_E13.5_s	female_E13.5_l	female_E13.5_p	female_E14.5_n	female_E14.5_s	female_E14.5_l	female_E14.5_p	female_E15.5_n	female_E15.5_s	female_E15.5_l	female_E15.5_p	female_E16.5_n	female_E16.5_s	female_E16.5_l	female_E16.5_p	male_E9.5_n	male_E9.5_s	male_E9.5_l	male_E9.5_p	male_E10.5_n	male_E10.5_s	male_E10.5_l	male_E10.5_p	male_E11.5_n	male_E11.5_s	male_E11.5_l	male_E11.5_p	male_E12.5_n	male_E12.5_s	male_E12.5_l	male_E12.5_p	male_E13.5_n	male_E13.5_s	male_E13.5_l	male_E13.5_p	male_E14.5_n	male_E14.5_s	male_E14.5_l	male_E14.5_p	male_E15.5_n	male_E15.5_s	male_E15.5_l	male_E15.5_p	male_E16.5_n	male_E16.5_s	male_E16.5_l	male_E16.5_p
0	ENSMUSG00000025993	9.675285	3.506512	7.051691e-18	ENSMUSG00000009927	10.553584	2.289993	6.425570e-22	ENSMUSG00000020422	11.005273	3.124363	7.866966e-24	ENSMUSG00000068048	10.203928	3.183214	4.156145e-20	ENSMUSG00000015053	8.693368	2.885315	5.389155e-14	ENSMUSG00000079184	10.776521	2.535967	9.699475e-23	ENSMUSG00000029110	10.640263	2.477743	4.225319e-22	ENSMUSG00000016427	13.642920	3.802901	2.427952e-38	ENSMUSG00000039678	9.339453	2.834671	1.294557e-16	ENSMUSG00000025362	7.666979	1.920825	2.640865e-10	ENSMUSG00000060126	10.975850	1.714372	7.146918e-24	ENSMUSG00000066652	10.123033	4.846027	9.531272e-20	ENSMUSG00000046323	10.158194	2.625564	6.650036e-20	ENSMUSG00000015656	11.121705	1.494284	2.147212e-24	ENSMUSG00000017009	12.410044	3.629442	5.031710e-31	ENSMUSG00000030720	13.479259	5.045060	3.275724e-37
1	ENSMUSG00000070047	9.271043	2.799711	1.341154e-16	ENSMUSG00000046330	10.513092	1.752015	6.425570e-22	ENSMUSG00000022443	10.840289	2.456336	2.420277e-23	ENSMUSG00000006200	10.006590	2.998120	1.556078e-19	ENSMUSG00000002324	8.654793	4.295401	5.389155e-14	ENSMUSG00000026239	10.708235	3.632599	1.016266e-22	ENSMUSG00000005683	10.555483	2.258651	5.229435e-22	ENSMUSG00000031397	13.155551	4.654744	1.150476e-35	ENSMUSG00000050966	9.317941	2.721676	1.294557e-16	ENSMUSG00000050621	7.574490	2.481963	2.640865e-10	ENSMUSG00000068457	10.951319	3.280838	7.146918e-24	ENSMUSG00000038793	9.452920	4.327497	2.905648e-17	ENSMUSG00000040726	9.197942	3.419736	3.982022e-16	ENSMUSG00000060985	10.904320	3.489329	7.996833e-24	ENSMUSG00000064215	11.843365	2.978279	2.541931e-28	ENSMUSG00000029223	13.394968	3.819025	4.363438e-37
2	ENSMUSG00000005672	9.175815	2.649677	2.445568e-16	ENSMUSG00000079641	10.497908	2.643158	6.425570e-22	ENSMUSG00000051391	10.632948	2.119815	1.523426e-22	ENSMUSG00000006585	9.358194	2.174949	5.901360e-17	ENSMUSG00000070858	8.400472	1.808149	3.235416e-13	ENSMUSG00000029848	10.583568	5.303363	1.938077e-22	ENSMUSG00000005470	10.458048	3.059304	9.793018e-22	ENSMUSG00000027855	12.862712	3.865922	2.654570e-34	ENSMUSG00000025993	9.178117	3.227013	2.393861e-16	ENSMUSG00000090733	7.573617	2.238895	2.640865e-10	ENSMUSG00000020402	10.336987	2.102242	3.487750e-21	ENSMUSG00000054405	9.432779	2.014945	2.905648e-17	ENSMUSG00000000579	9.014544	1.386154	1.438324e-15	ENSMUSG00000006273	10.290380	2.412115	2.832716e-21	ENSMUSG00000058550	11.744291	2.830286	5.496978e-28	ENSMUSG00000028784	13.372955	6.049841	4.363438e-37
3	ENSMUSG00000020372	9.093369	1.251233	4.191537e-16	ENSMUSG00000047675	10.284691	2.171756	2.968842e-21	ENSMUSG00000031386	10.483914	1.943355	5.588559e-22	ENSMUSG00000034708	9.262971	2.367940	8.937772e-17	ENSMUSG00000029848	8.314445	4.209333	5.030145e-13	ENSMUSG00000022432	10.550991	4.120283	1.952663e-22	ENSMUSG00000085601	10.363777	4.782912	1.977321e-21	ENSMUSG00000078938	12.790547	4.348732	5.051986e-34	ENSMUSG00000028640	9.147747	2.451515	2.537739e-16	ENSMUSG00000047675	7.524755	2.484592	2.882207e-10	ENSMUSG00000059005	10.126877	2.002101	2.290985e-20	ENSMUSG00000063856	9.221128	2.376962	1.604226e-16	ENSMUSG00000024521	8.907993	3.335130	2.835564e-15	ENSMUSG00000062382	10.274092	1.419304	2.875193e-21	ENSMUSG00000076617	11.666992	3.978752	1.025600e-27	ENSMUSG00000000730	13.361144	5.864875	4.363438e-37
4	ENSMUSG00000078193	9.026900	1.157818	6.424539e-16	ENSMUSG00000086290	10.277099	3.083502	2.968842e-21	ENSMUSG00000015023	9.752338	2.159153	7.158992e-19	ENSMUSG00000005566	9.259839	2.052994	8.937772e-17	ENSMUSG00000062683	7.164023	0.858845	3.419982e-09	ENSMUSG00000051455	10.532824	4.049201	1.952663e-22	ENSMUSG00000024841	10.309999	2.448106	2.772121e-21	ENSMUSG00000031432	12.599156	5.854750	3.583250e-33	ENSMUSG00000070047	9.027536	2.639028	6.387309e-16	ENSMUSG00000060636	7.438374	1.920056	4.246982e-10	ENSMUSG00000031523	9.925300	2.433719	1.410597e-19	ENSMUSG00000030246	9.075022	2.691470	4.961495e-16	ENSMUSG00000030246	8.584466	2.479161	3.511602e-14	ENSMUSG00000000902	10.227734	1.715963	4.064325e-21	ENSMUSG00000052374	11.327856	3.041988	4.169558e-26	ENSMUSG00000020265	13.283297	2.420750	9.022987e-37
5	ENSMUSG00000030539	8.804487	2.545866	4.098870e-15	ENSMUSG00000022114	10.276466	3.153666	2.968842e-21	ENSMUSG00000024949	9.728806	1.383166	7.158992e-19	ENSMUSG00000095180	8.953495	2.261455	1.252871e-15	ENSMUSG00000031158	6.938035	1.707143	1.239741e-08	ENSMUSG00000027879	10.530318	2.893320	1.952663e-22	ENSMUSG00000022837	10.272038	3.318377	3.426621e-21	ENSMUSG00000096210	12.522547	4.370077	8.592425e-33	ENSMUSG00000021938	8.991473	1.718026	7.606361e-16	ENSMUSG00000053332	7.414816	2.510639	4.246982e-10	ENSMUSG00000022048	9.797847	2.392932	4.183906e-19	ENSMUSG00000005732	9.036788	1.686583	5.869387e-16	ENSMUSG00000096255	8.578009	1.428677	3.511602e-14	ENSMUSG00000052374	10.132510	3.135312	9.612178e-21	ENSMUSG00000000579	11.205919	1.560410	1.387367e-25	ENSMUSG00000031765	13.253231	4.543951	1.179177e-36
6	ENSMUSG00000020257	8.785953	2.077950	4.230204e-15	ENSMUSG00000062328	10.267609	1.556812	2.968842e-21	ENSMUSG00000006498	9.727760	1.708000	7.158992e-19	ENSMUSG00000015176	8.847936	1.630474	2.779926e-15	ENSMUSG00000031146	6.799439	1.394861	2.547150e-08	ENSMUSG00000085601	10.498368	4.777515	2.397870e-22	ENSMUSG00000067702	10.237240	3.304119	4.210562e-21	ENSMUSG00000020059	12.518625	3.704074	8.592425e-33	ENSMUSG00000081683	8.955410	2.592590	9.234905e-16	ENSMUSG00000067288	7.350248	2.200550	5.397799e-10	ENSMUSG00000046434	9.758918	1.859327	5.268045e-19	ENSMUSG00000016921	8.873612	1.467029	2.207980e-15	ENSMUSG00000063856	8.505037	2.090800	5.660610e-14	ENSMUSG00000058550	10.121861	2.819745	9.646129e-21	ENSMUSG00000051316	11.117189	2.012710	3.226688e-25	ENSMUSG00000067338	13.120084	3.900148	5.795057e-36
7	ENSMUSG00000050966	8.717567	2.552437	6.893533e-15	ENSMUSG00000034892	10.248628	2.115961	2.968842e-21	ENSMUSG00000035621	9.588923	2.231044	2.428498e-18	ENSMUSG00000004264	8.711679	1.316128	8.168981e-15	ENSMUSG00000022982	6.701124	1.090657	3.761935e-08	ENSMUSG00000009549	10.439480	2.180382	3.970213e-22	ENSMUSG00000021276	10.215096	2.184067	4.630386e-21	ENSMUSG00000051592	12.486465	4.781847	1.207292e-32	ENSMUSG00000027597	8.888977	1.826851	1.346158e-15	ENSMUSG00000025290	7.326690	1.644644	5.721278e-10	ENSMUSG00000069045	9.663995	2.637060	1.169947e-18	ENSMUSG00000055044	8.439387	2.954699	8.703633e-14	ENSMUSG00000041359	8.476624	2.286723	6.325031e-14	ENSMUSG00000024312	10.049816	1.978088	1.826304e-20	ENSMUSG00000056155	11.077995	3.290473	4.376982e-25	ENSMUSG00000040013	13.116325	2.764854	5.795057e-36
8	ENSMUSG00000028640	8.644069	2.261034	1.184027e-14	ENSMUSG00000057322	10.246730	2.125862	2.968842e-21	ENSMUSG00000022463	9.520420	1.790144	4.183043e-18	ENSMUSG00000001270	8.385288	2.722079	1.227204e-13	ENSMUSG00000021361	6.578230	1.272332	7.422451e-08	ENSMUSG00000051159	10.386230	3.689216	5.797064e-22	ENSMUSG00000028955	10.201809	2.687172	4.719814e-21	ENSMUSG00000005883	12.137934	3.430297	6.910178e-31	ENSMUSG00000078193	8.861139	1.165304	1.571566e-15	ENSMUSG00000041453	7.282190	1.754996	6.663122e-10	ENSMUSG00000002265	9.607468	2.474684	1.803270e-18	ENSMUSG00000026520	8.415492	2.413517	9.488688e-14	ENSMUSG00000029447	8.410110	1.212361	9.934387e-14	ENSMUSG00000037058	10.003457	1.438324	2.676849e-20	ENSMUSG00000038845	11.005595	1.506394	8.709848e-25	ENSMUSG00000059625	13.014854	5.356165	1.998837e-35
9	ENSMUSG00000034165	8.601248	1.857363	1.564681e-14	ENSMUSG00000025362	10.110069	1.593437	1.088060e-20	ENSMUSG00000025278	9.403545	2.218333	1.046798e-17	ENSMUSG00000051695	8.318882	1.869896	1.823702e-13	ENSMUSG00000015217	6.561844	1.122764	7.733503e-08	ENSMUSG00000028618	10.384351	2.941988	5.797064e-22	ENSMUSG00000079507	10.163531	3.717565	6.295709e-21	ENSMUSG00000071103	12.028642	4.128081	2.285098e-30	ENSMUSG00000005672	8.780787	2.482570	2.952727e-15	ENSMUSG00000071072	7.247289	2.443662	6.663122e-10	ENSMUSG00000051391	9.453352	1.825916	7.162547e-18	ENSMUSG00000016344	8.365651	1.624217	1.304898e-13	ENSMUSG00000032346	8.271593	2.783371	2.885108e-13	ENSMUSG00000028378	9.963364	2.873443	3.701773e-20	ENSMUSG00000044595	10.982188	2.406122	1.016071e-24	ENSMUSG00000060743	12.981567	1.988274	2.831350e-35
10	ENSMUSG00000031169	8.540531	2.480062	2.430180e-14	ENSMUSG00000020180	10.041105	2.036696	1.995005e-20	ENSMUSG00000036617	9.304712	2.919814	2.392999e-17	ENSMUSG00000030605	8.305099	1.721778	1.823702e-13	ENSMUSG00000057762	6.403448	1.297241	2.072295e-07	ENSMUSG00000030041	10.332980	4.657975	9.091505e-22	ENSMUSG00000030041	10.127151	4.817700	7.547670e-21	ENSMUSG00000000563	12.012431	2.280046	2.669202e-30	ENSMUSG00000019989	8.713089	2.347644	4.964856e-15	ENSMUSG00000034892	7.246416	2.386563	6.663122e-10	ENSMUSG00000032249	9.426688	1.706003	8.398271e-18	ENSMUSG00000004864	8.294646	2.318275	2.038660e-13	ENSMUSG00000025393	8.173114	0.982696	5.963324e-13	ENSMUSG00000039756	9.927654	1.627596	4.359673e-20	ENSMUSG00000028964	10.956603	1.205996	1.225782e-24	ENSMUSG00000004032	12.935396	3.435295	4.430194e-35
11	ENSMUSG00000063229	8.520719	1.935022	2.662324e-14	ENSMUSG00000090862	10.025921	2.058720	2.099006e-20	ENSMUSG00000002265	9.298437	2.550425	2.392999e-17	ENSMUSG00000060860	8.304474	1.568107	1.823702e-13	ENSMUSG00000002010	6.360435	1.100290	2.583075e-07	ENSMUSG00000000346	10.321704	3.197427	9.438771e-22	ENSMUSG00000051159	10.122723	3.529925	7.547670e-21	ENSMUSG00000009596	11.981579	3.759052	3.725632e-30	ENSMUSG00000071235	8.692210	2.467097	5.288858e-15	ENSMUSG00000059291	7.242054	1.749781	6.663122e-10	ENSMUSG00000021840	9.409623	1.474293	9.056714e-18	ENSMUSG00000009013	8.291232	1.059263	2.038660e-13	ENSMUSG00000027404	7.894790	1.046342	5.289501e-12	ENSMUSG00000022234	9.926402	1.220191	4.359673e-20	ENSMUSG00000026354	10.699393	3.614129	1.863430e-23	ENSMUSG00000021681	12.848689	3.183676	1.265890e-34
12	ENSMUSG00000039678	8.486526	2.667356	3.282079e-14	ENSMUSG00000041841	10.019594	1.709529	2.099006e-20	ENSMUSG00000040661	9.267846	1.957644	2.961350e-17	ENSMUSG00000018677	8.214262	1.428880	3.584215e-13	ENSMUSG00000079435	6.299671	0.963320	3.554708e-07	ENSMUSG00000025480	10.280356	3.648048	1.347149e-21	ENSMUSG00000058076	10.120192	2.276411	7.547670e-21	ENSMUSG00000045193	11.940790	2.441214	5.863443e-30	ENSMUSG00000034165	8.689680	1.838443	5.288858e-15	ENSMUSG00000058546	7.238564	1.563402	6.663122e-10	ENSMUSG00000039202	9.393092	2.016654	9.782577e-18	ENSMUSG00000078812	8.264606	0.984336	2.353244e-13	ENSMUSG00000059796	7.874125	1.199098	5.739314e-12	ENSMUSG00000024966	9.882549	1.388733	6.046914e-20	ENSMUSG00000024767	10.667004	1.594718	2.438491e-23	ENSMUSG00000020799	12.844126	5.489536	1.265890e-34
13	ENSMUSG00000026701	8.479815	2.124991	3.282079e-14	ENSMUSG00000063511	9.978469	2.008853	2.844759e-20	ENSMUSG00000048154	9.238300	1.852573	3.644287e-17	ENSMUSG00000047945	8.204239	1.440769	3.617937e-13	ENSMUSG00000022450	6.292161	0.923928	3.554708e-07	ENSMUSG00000042682	10.232119	2.369933	1.922453e-21	ENSMUSG00000009291	10.102476	2.698932	8.397850e-21	ENSMUSG00000045106	11.736325	3.262661	5.329347e-29	ENSMUSG00000026475	8.630207	2.609254	8.354422e-15	ENSMUSG00000063457	7.228093	1.895178	6.663122e-10	ENSMUSG00000025278	9.385093	2.201102	9.800496e-18	ENSMUSG00000028945	8.252999	1.552269	2.366319e-13	ENSMUSG00000035557	7.858627	3.381784	5.739314e-12	ENSMUSG00000050708	9.881923	1.407021	6.046914e-20	ENSMUSG00000028378	10.367062	2.726956	5.458732e-22	ENSMUSG00000024164	12.821845	5.539739	1.452046e-34
14	ENSMUSG00000003153	8.471507	2.583922	3.304594e-14	ENSMUSG00000060636	9.975306	1.495595	2.844759e-20	ENSMUSG00000066232	9.219998	1.863410	4.053082e-17	ENSMUSG00000054405	8.092727	1.359733	8.489766e-13	ENSMUSG00000048450	6.219790	2.874785	4.519880e-07	ENSMUSG00000025066	10.225227	1.620489	1.922453e-21	ENSMUSG00000030760	10.071791	3.381085	1.071341e-20	ENSMUSG00000020774	11.713317	4.867268	6.793391e-29	ENSMUSG00000020782	8.599838	2.398123	9.895918e-15	ENSMUSG00000049517	7.222858	1.704399	6.663122e-10	ENSMUSG00000071172	9.250175	1.145529	3.261344e-17	ENSMUSG00000003873	8.246854	1.442206	2.366319e-13	ENSMUSG00000009013	7.856689	1.017823	5.739314e-12	ENSMUSG00000005312	9.848093	2.068751	8.025063e-20	ENSMUSG00000060376	10.345286	1.825553	6.396738e-22	ENSMUSG00000038393	12.818892	3.154333	1.452046e-34
15	ENSMUSG00000035202	8.458085	1.889859	3.489795e-14	ENSMUSG00000067288	9.937977	1.750292	3.881748e-20	ENSMUSG00000026193	9.209278	2.172497	4.215514e-17	ENSMUSG00000010760	8.044489	2.428791	1.181471e-12	ENSMUSG00000010592	6.217741	1.813606	4.519880e-07	ENSMUSG00000047260	10.218336	1.998312	1.922453e-21	ENSMUSG00000029848	10.041739	4.620284	1.362796e-20	ENSMUSG00000026864	11.689785	1.932202	8.715647e-29	ENSMUSG00000027547	8.597307	2.050077	9.895918e-15	ENSMUSG00000009927	7.220240	2.737736	6.663122e-10	ENSMUSG00000066232	9.237376	1.884769	3.446165e-17	ENSMUSG00000034681	8.158097	1.064744	4.642911e-13	ENSMUSG00000017009	7.810841	2.454726	7.749663e-12	ENSMUSG00000035960	9.828672	1.477400	9.246805e-20	ENSMUSG00000074397	10.317525	2.161304	8.010176e-22	ENSMUSG00000000739	12.748829	5.672569	3.139751e-34
16	ENSMUSG00000070327	8.425810	2.346476	4.115695e-14	ENSMUSG00000061983	9.917731	2.684566	4.475692e-20	ENSMUSG00000029094	9.202742	2.344470	4.231190e-17	ENSMUSG00000020358	7.957410	1.712948	2.255607e-12	ENSMUSG00000041765	6.213645	1.453722	4.519880e-07	ENSMUSG00000029148	10.216457	2.618429	1.922453e-21	ENSMUSG00000040652	10.012002	2.122159	1.733229e-20	ENSMUSG00000020664	11.684556	2.292654	9.018495e-29	ENSMUSG00000005360	8.589082	2.282080	1.007107e-14	ENSMUSG00000008683	7.189702	1.909107	7.873872e-10	ENSMUSG00000026421	9.103257	2.130830	1.125534e-16	ENSMUSG00000018293	8.117133	1.487397	6.128581e-13	ENSMUSG00000022234	7.744973	0.872920	1.227481e-11	ENSMUSG00000064215	9.802361	2.772730	1.063357e-19	ENSMUSG00000041881	10.231516	1.157656	1.839369e-21	ENSMUSG00000016253	12.739970	2.557996	3.364487e-34
17	ENSMUSG00000067860	8.364774	2.241654	5.975660e-14	ENSMUSG00000093674	9.895587	1.917257	5.275476e-20	ENSMUSG00000091405	9.176072	2.721152	5.136276e-17	ENSMUSG00000020733	7.919821	1.857206	2.884218e-12	ENSMUSG00000096472	6.199307	2.272567	4.760982e-07	ENSMUSG00000008892	10.194530	1.830856	2.289167e-21	ENSMUSG00000000346	9.981000	3.043229	2.147846e-20	ENSMUSG00000112858	11.660239	3.089995	1.168716e-28	ENSMUSG00000031995	8.530242	2.487493	1.517931e-14	ENSMUSG00000079641	7.173123	2.744446	8.421299e-10	ENSMUSG00000027878	9.091259	2.118546	1.187135e-16	ENSMUSG00000060461	7.983315	2.251044	1.727418e-12	ENSMUSG00000060461	7.659086	2.121009	2.270750e-11	ENSMUSG00000068039	9.800482	1.115191	1.063357e-19	ENSMUSG00000031781	10.098692	1.472313	6.788683e-21	ENSMUSG00000011589	12.721179	4.018242	4.019609e-34
18	ENSMUSG00000070780	8.344322	2.300981	6.797062e-14	ENSMUSG00000071415	9.864585	1.624612	6.809910e-20	ENSMUSG00000070047	9.163522	2.196258	5.323093e-17	ENSMUSG00000030057	7.865319	1.018877	4.229372e-12	ENSMUSG00000063714	6.146054	1.457917	6.421541e-07	ENSMUSG00000011349	10.173857	4.094938	2.696441e-21	ENSMUSG00000046338	9.967713	3.236759	2.293390e-20	ENSMUSG00000112920	11.495779	4.146809	6.871828e-28	ENSMUSG00000048087	8.465708	1.645417	2.525999e-14	ENSMUSG00000038900	7.165270	2.115061	8.472500e-10	ENSMUSG00000040661	9.062729	1.890641	1.461576e-16	ENSMUSG00000021377	7.931427	1.727744	2.488763e-12	ENSMUSG00000067148	7.646816	1.315973	2.366693e-11	ENSMUSG00000076617	9.771664	3.496658	1.354955e-19	ENSMUSG00000037772	10.064941	1.531571	9.067885e-21	ENSMUSG00000025372	12.719569	3.497971	4.019609e-34
19	ENSMUSG00000074637	8.305975	1.968261	9.003900e-14	ENSMUSG00000025508	9.836114	1.719935	8.588026e-20	ENSMUSG00000032249	9.161430	1.748122	5.323093e-17	ENSMUSG00000034681	7.792648	0.928247	6.706913e-12	ENSMUSG00000022884	6.093482	1.319857	8.612351e-07	ENSMUSG00000003824	10.145666	2.342198	3.436836e-21	ENSMUSG00000011349	9.963284	4.276134	2.293390e-20	ENSMUSG00000015365	11.460743	2.944228	9.856589e-28	ENSMUSG00000059288	8.435655	2.165314	3.052583e-14	ENSMUSG00000041841	7.146947	1.880954	8.859246e-10	ENSMUSG00000024590	9.029665	2.010623	1.879276e-16	ENSMUSG00000028851	7.917089	1.261961	2.653457e-12	ENSMUSG00000047751	7.637775	1.748506	2.411955e-11	ENSMUSG00000025613	9.736582	1.347613	1.837981e-19	ENSMUSG00000096255	10.052422	1.493729	9.782591e-21	ENSMUSG00000054932	12.694604	4.254623	5.318218e-34
20	ENSMUSG00000027597	8.298944	1.738588	9.170836e-14	ENSMUSG00000012848	9.808908	1.425149	1.071368e-19	ENSMUSG00000031523	9.155678	2.242032	5.359283e-17	ENSMUSG00000070837	7.741904	2.773221	9.174379e-12	ENSMUSG00000007872	6.015650	1.914248	1.303557e-06	ENSMUSG00000024099	10.134390	2.174072	3.689660e-21	ENSMUSG00000020097	9.941773	2.648637	2.541836e-20	ENSMUSG00000025090	11.404789	2.966070	1.837804e-27	ENSMUSG00000070780	8.433440	2.317370	3.052583e-14	ENSMUSG00000093674	7.139967	2.213113	8.859246e-10	ENSMUSG00000035530	9.006468	0.932917	2.211797e-16	ENSMUSG00000026305	7.905483	2.512095	2.774082e-12	ENSMUSG00000025491	7.575782	1.895103	3.541630e-11	ENSMUSG00000002524	9.695234	1.784542	2.652068e-19	ENSMUSG00000021037	9.962602	1.354744	2.309202e-20	ENSMUSG00000027510	12.673397	2.866393	6.712946e-34
21	ENSMUSG00000054091	8.252929	1.297691	1.257517e-13	ENSMUSG00000061787	9.738048	1.885617	2.044133e-19	ENSMUSG00000031691	9.143389	2.174271	5.544597e-17	ENSMUSG00000024827	7.738145	2.777019	9.174379e-12	ENSMUSG00000062054	5.947375	1.306615	1.802223e-06	ENSMUSG00000032065	10.099934	3.157649	4.760814e-21	ENSMUSG00000031158	9.932283	2.365777	2.541836e-20	ENSMUSG00000031431	11.391716	3.475548	2.091035e-27	ENSMUSG00000037013	8.425216	2.001610	3.143846e-14	ENSMUSG00000054766	7.084997	2.633518	1.264578e-09	ENSMUSG00000020186	8.938743	1.856368	3.905296e-16	ENSMUSG00000061104	7.819457	1.235613	5.263451e-12	ENSMUSG00000039329	7.506686	1.750091	5.597424e-11	ENSMUSG00000019139	9.664537	1.985692	3.448191e-19	ENSMUSG00000060985	9.949537	2.397631	2.513664e-20	ENSMUSG00000048997	12.668565	4.358173	6.884500e-34
22	ENSMUSG00000052609	8.248455	2.208777	1.257517e-13	ENSMUSG00000057113	9.734251	1.819364	2.044133e-19	ENSMUSG00000046434	9.142605	1.722893	5.544597e-17	ENSMUSG00000037169	7.719978	1.880798	1.015828e-11	ENSMUSG00000022914	5.923138	1.339969	2.027700e-06	ENSMUSG00000057054	10.097427	4.349154	4.760814e-21	ENSMUSG00000022404	9.926589	2.894901	2.541836e-20	ENSMUSG00000060445	11.356419	2.942189	2.949304e-27	ENSMUSG00000028766	8.387887	2.034470	4.099324e-14	ENSMUSG00000040459	7.036135	2.753390	1.659235e-09	ENSMUSG00000035203	8.932343	2.009212	3.958092e-16	ENSMUSG00000045394	7.787368	1.891863	6.493005e-12	ENSMUSG00000021273	7.504748	1.541148	5.597424e-11	ENSMUSG00000037958	9.583097	1.847547	6.631038e-19	ENSMUSG00000005413	9.925313	2.877556	3.066094e-20	ENSMUSG00000020661	12.647626	3.678220	8.678565e-34
23	ENSMUSG00000020649	8.238229	2.123487	1.285287e-13	ENSMUSG00000037563	9.712740	1.252711	2.419954e-19	ENSMUSG00000060206	9.129532	2.132005	6.006475e-17	ENSMUSG00000005732	7.707448	1.293624	1.077566e-11	ENSMUSG00000001018	5.816630	1.378490	3.640613e-06	ENSMUSG00000052525	10.076128	3.739043	5.695055e-21	ENSMUSG00000039231	9.923426	2.583941	2.541836e-20	ENSMUSG00000030309	11.256539	3.022508	8.234447e-27	ENSMUSG00000021464	8.385039	2.382290	4.099324e-14	ENSMUSG00000108414	7.023920	2.398438	1.743972e-09	ENSMUSG00000074578	8.915278	1.716906	4.415067e-16	ENSMUSG00000006442	7.774396	1.732622	6.894448e-12	ENSMUSG00000026641	7.483115	1.918973	6.168533e-11	ENSMUSG00000041438	9.526088	1.925068	1.080251e-18	ENSMUSG00000062382	9.913065	1.096484	3.321934e-20	ENSMUSG00000036912	12.639036	4.858409	9.358019e-34
24	ENSMUSG00000021255	8.236951	2.166359	1.285287e-13	ENSMUSG00000020460	9.676676	1.311521	3.307507e-19	ENSMUSG00000022048	9.114367	2.118188	6.643078e-17	ENSMUSG00000033751	7.627887	1.336339	1.929125e-11	ENSMUSG00000028843	5.793758	1.316504	3.831754e-06	ENSMUSG00000002076	10.026011	2.939042	8.817744e-21	ENSMUSG00000027879	9.922792	2.716861	2.541836e-20	ENSMUSG00000039217	11.214967	3.770557	1.273794e-26	ENSMUSG00000016319	8.376498	2.116309	4.250469e-14	ENSMUSG00000039001	7.000361	1.926194	1.990186e-09	ENSMUSG00000089774	8.911012	1.891867	4.415067e-16	ENSMUSG00000036438	7.743672	1.141142	8.432697e-12	ENSMUSG00000066705	7.481501	2.452103	6.168533e-11	ENSMUSG00000031880	9.499777	3.859593	1.350331e-18	ENSMUSG00000067764	9.868427	3.076215	4.981089e-20	ENSMUSG00000018012	12.612729	4.476464	1.225466e-33
25	ENSMUSG00000031995	8.217138	2.351220	1.467461e-13	ENSMUSG00000063316	9.668451	1.439178	3.423025e-19	ENSMUSG00000035203	9.101294	2.193334	7.216039e-17	ENSMUSG00000010660	7.618490	2.258573	2.000761e-11	ENSMUSG00000030605	5.790344	1.424774	3.831754e-06	ENSMUSG00000030086	10.008470	2.726532	1.017869e-20	ENSMUSG00000061232	9.922476	4.666304	2.541836e-20	ENSMUSG00000028109	11.208169	2.533911	1.352570e-26	ENSMUSG00000010660	8.361946	2.426985	4.643266e-14	ENSMUSG00000028936	6.961097	1.984537	2.541218e-09	ENSMUSG00000035621	8.862217	2.697753	6.417325e-16	ENSMUSG00000004264	7.725921	1.235857	9.322425e-12	ENSMUSG00000047407	7.458253	2.308014	7.087879e-11	ENSMUSG00000005656	9.493512	1.707079	1.393058e-18	ENSMUSG00000031590	9.842299	1.611926	6.212358e-20	ENSMUSG00000004937	12.607897	2.150565	1.263468e-33
26	ENSMUSG00000063445	8.202438	1.974003	1.606740e-13	ENSMUSG00000090733	9.665288	1.592464	3.423025e-19	ENSMUSG00000031167	9.085867	1.238898	8.019508e-17	ENSMUSG00000016921	7.584661	1.014088	2.509041e-11	ENSMUSG00000054717	5.778738	1.275893	3.911353e-06	ENSMUSG00000061232	9.995314	4.525886	1.089857e-20	ENSMUSG00000022940	9.921527	2.774864	2.541836e-20	ENSMUSG00000021114	11.151954	2.227754	2.426988e-26	ENSMUSG00000067878	8.327148	2.419141	6.025182e-14	ENSMUSG00000045128	6.949754	1.402465	2.662343e-09	ENSMUSG00000029581	8.860884	2.104371	6.417325e-16	ENSMUSG00000054766	7.667888	1.942161	1.413512e-11	ENSMUSG00000095677	7.440817	1.270471	7.605369e-11	ENSMUSG00000033628	9.482862	2.427002	1.500020e-18	ENSMUSG00000021178	9.824879	1.394413	7.112353e-20	ENSMUSG00000038080	12.595549	4.368733	1.431629e-33
27	ENSMUSG00000021464	8.155144	2.219463	2.238973e-13	ENSMUSG00000030432	9.654532	1.688060	3.666168e-19	ENSMUSG00000035150	9.054230	1.757601	1.035163e-16	ENSMUSG00000029591	7.549578	1.644684	3.177064e-11	ENSMUSG00000034932	5.641506	0.921757	7.998588e-06	ENSMUSG00000022837	9.974640	3.170303	1.263488e-20	ENSMUSG00000032387	9.920895	3.214711	2.541836e-20	ENSMUSG00000022432	11.149601	3.171072	2.453094e-26	ENSMUSG00000020372	8.320822	1.135716	6.150679e-14	ENSMUSG00000012848	6.928813	1.572026	2.988092e-09	ENSMUSG00000013415	8.849686	1.755305	6.841699e-16	ENSMUSG00000056201	7.643992	1.235667	1.641613e-11	ENSMUSG00000052374	7.439526	2.309771	7.605369e-11	ENSMUSG00000032399	9.472212	0.784287	1.616260e-18	ENSMUSG00000073102	9.748669	3.542623	1.456981e-19	ENSMUSG00000053398	12.593401	3.908803	1.431629e-33
28	ENSMUSG00000032518	8.147474	1.053758	2.317433e-13	ENSMUSG00000024608	9.615305	1.228392	5.075292e-19	ENSMUSG00000063382	9.022331	2.710283	1.339657e-16	ENSMUSG00000028640	7.545193	1.939182	3.179829e-11	ENSMUSG00000079641	5.613514	1.335702	8.321906e-06	ENSMUSG00000024786	9.932040	3.540508	1.883436e-20	ENSMUSG00000030291	9.879769	2.149617	3.586699e-20	ENSMUSG00000036499	11.128684	3.337958	3.008452e-26	ENSMUSG00000037188	8.308484	2.370989	6.497343e-14	ENSMUSG00000057278	6.918343	1.964693	3.116886e-09	ENSMUSG00000055065	8.840087	1.610833	7.198736e-16	ENSMUSG00000033845	7.559332	1.375842	3.049346e-11	ENSMUSG00000038845	7.404009	1.229870	9.613591e-11	ENSMUSG00000030357	9.468452	1.372171	1.631390e-18	ENSMUSG00000032047	9.696410	2.143185	2.350499e-19	ENSMUSG00000007564	12.577294	2.377677	1.706786e-33
29	ENSMUSG00000027547	8.141083	1.977915	2.375252e-13	ENSMUSG00000071528	9.614039	1.834906	5.075292e-19	ENSMUSG00000025332	8.997754	1.819495	1.622119e-16	ENSMUSG00000028333	7.531411	1.318298	3.416978e-11	ENSMUSG00000025920	5.603956	2.145765	8.468653e-06	ENSMUSG00000051977	9.919511	3.911473	2.032783e-20	ENSMUSG00000028212	9.876606	2.765891	3.586699e-20	ENSMUSG00000006941	11.103060	1.721562	3.891011e-26	ENSMUSG00000029581	8.306903	2.275014	6.497343e-14	ENSMUSG00000020460	6.880824	1.592141	3.935976e-09	ENSMUSG00000062825	8.808090	1.581099	9.261646e-16	ENSMUSG00000102252	7.548408	1.742796	3.205738e-11	ENSMUSG00000031353	7.283897	1.293793	2.283477e-10	ENSMUSG00000051965	9.460309	3.437526	1.718373e-18	ENSMUSG00000053909	9.598425	2.753197	5.906040e-19	ENSMUSG00000027602	12.558504	2.307501	2.106130e-33
30	ENSMUSG00000031826	8.136609	1.766640	2.398037e-13	ENSMUSG00000078974	9.609612	1.558262	5.127482e-19	ENSMUSG00000070733	8.978144	1.954974	1.878300e-16	ENSMUSG00000033845	7.527652	1.245281	3.416978e-11	ENSMUSG00000035048	5.548653	0.809555	1.102372e-05	ENSMUSG00000031715	9.918884	2.322992	2.032783e-20	ENSMUSG00000017707	9.845604	2.668066	4.626007e-20	ENSMUSG00000022295	11.074299	1.966723	5.288555e-26	ENSMUSG00000024406	8.250593	1.771610	9.916722e-14	ENSMUSG00000032330	6.833706	1.706198	5.255476e-09	ENSMUSG00000024420	8.751296	1.942124	1.485051e-15	ENSMUSG00000041891	7.482864	1.680250	5.120177e-11	ENSMUSG00000054612	7.264847	1.715909	2.547356e-10	ENSMUSG00000071341	9.440888	3.372850	2.016991e-18	ENSMUSG00000025357	9.564674	2.725935	7.924946e-19	ENSMUSG00000029634	12.547229	2.804089	2.364577e-33
31	ENSMUSG00000021938	8.125105	1.673141	2.567405e-13	ENSMUSG00000008683	9.569752	1.510148	6.975404e-19	ENSMUSG00000015165	8.916700	1.606960	3.177410e-16	ENSMUSG00000030127	7.481293	1.091225	4.724570e-11	ENSMUSG00000051159	5.506665	1.613639	1.330341e-05	ENSMUSG00000032409	9.913873	3.000286	2.040569e-20	ENSMUSG00000038187	9.844972	2.701699	4.626007e-20	ENSMUSG00000060771	11.065932	4.139163	5.642558e-26	ENSMUSG00000042348	8.247113	1.986505	9.916722e-14	ENSMUSG00000090862	6.795315	2.253257	6.552724e-09	ENSMUSG00000101609	8.733965	1.884725	1.677251e-15	ENSMUSG00000033751	7.442583	1.412540	6.564459e-11	ENSMUSG00000029430	7.218675	0.828643	3.472257e-10	ENSMUSG00000005413	9.384192	3.126902	3.375209e-18	ENSMUSG00000006057	9.538546	1.169089	9.880095e-19	ENSMUSG00000025487	12.510721	1.873924	3.650667e-33
32	ENSMUSG00000006356	8.112323	1.901815	2.724186e-13	ENSMUSG00000069309	9.568486	3.214732	6.975404e-19	ENSMUSG00000046062	8.912517	1.807629	3.202607e-16	ENSMUSG00000030541	7.473462	1.946297	4.871277e-11	ENSMUSG00000031383	5.420298	1.411387	2.061435e-05	ENSMUSG00000031010	9.913246	2.208581	2.040569e-20	ENSMUSG00000023832	9.823461	2.207152	5.564456e-20	ENSMUSG00000035540	11.019392	4.023561	9.341166e-26	ENSMUSG00000012396	8.246164	2.168669	9.916722e-14	ENSMUSG00000092203	6.773501	2.693801	7.415420e-09	ENSMUSG00000022443	8.710501	1.765680	1.940703e-15	ENSMUSG00000059552	7.441900	1.903165	6.564459e-11	ENSMUSG00000034341	7.208988	1.531767	3.515340e-10	ENSMUSG00000059447	9.372603	1.856373	3.677604e-18	ENSMUSG00000019139	9.451447	1.730328	2.210318e-18	ENSMUSG00000025401	12.485488	6.166241	4.888850e-33
33	ENSMUSG00000020721	8.111684	1.865416	2.724186e-13	ENSMUSG00000050856	9.567221	1.704452	6.975404e-19	ENSMUSG00000059273	8.907810	1.995704	3.246013e-16	ENSMUSG00000020349	7.456861	1.974145	5.372363e-11	ENSMUSG00000016308	5.405277	1.078252	2.206853e-05	ENSMUSG00000022940	9.891946	2.949537	2.461928e-20	ENSMUSG00000026277	9.802582	2.959312	6.638305e-20	ENSMUSG00000009670	10.983832	2.860775	1.312806e-25	ENSMUSG00000024036	8.233827	2.273760	1.069675e-13	ENSMUSG00000046330	6.759541	1.934832	7.841314e-09	ENSMUSG00000005161	8.708901	1.741498	1.940703e-15	ENSMUSG00000021963	7.396156	1.043512	8.999340e-11	ENSMUSG00000025739	7.196719	1.772803	3.739621e-10	ENSMUSG00000056155	9.335642	2.789213	5.095413e-18	ENSMUSG00000029223	9.443282	2.134849	2.319313e-18	ENSMUSG00000026809	12.394754	5.108266	1.449932e-32
34	ENSMUSG00000017499	8.070780	2.396002	3.718605e-13	ENSMUSG00000031939	9.565323	2.116014	6.975404e-19	ENSMUSG00000013415	8.827803	1.795891	6.297537e-16	ENSMUSG00000002870	7.399852	1.602194	8.042729e-11	ENSMUSG00000036199	5.395719	0.640289	2.241259e-05	ENSMUSG00000053012	9.823661	2.598363	4.740699e-20	ENSMUSG00000037315	9.800051	3.168722	6.638305e-20	ENSMUSG00000073411	10.968668	3.795732	1.500853e-25	ENSMUSG00000031826	8.207570	1.775124	1.296464e-13	ENSMUSG00000046364	6.757796	1.699039	7.841314e-09	ENSMUSG00000042406	8.707301	1.671174	1.940703e-15	ENSMUSG00000024837	7.357240	2.100099	1.170874e-10	ENSMUSG00000074397	7.190261	1.711933	3.785402e-10	ENSMUSG00000030007	9.322486	1.201689	5.637622e-18	ENSMUSG00000032380	9.418242	2.928796	2.860606e-18	ENSMUSG00000038375	12.308584	3.489920	3.950145e-32
35	ENSMUSG00000015112	8.030516	1.908452	5.044323e-13	ENSMUSG00000073702	9.531790	1.503407	9.373117e-19	ENSMUSG00000049539	8.813684	2.471850	6.955918e-16	ENSMUSG00000034330	7.391082	1.801297	8.365438e-11	ENSMUSG00000068523	5.395719	0.609472	2.241259e-05	ENSMUSG00000027722	9.810505	2.591710	5.272754e-20	ENSMUSG00000027509	9.786764	1.678593	7.371625e-20	ENSMUSG00000025037	10.914284	3.518442	2.654354e-25	ENSMUSG00000013089	8.204407	1.991545	1.296929e-13	ENSMUSG00000071415	6.745580	1.857625	8.316997e-09	ENSMUSG00000024393	8.681170	1.226289	2.365616e-15	ENSMUSG00000030127	7.292379	1.184795	1.846373e-10	ENSMUSG00000078812	7.187678	0.756343	3.785402e-10	ENSMUSG00000010796	9.292728	2.308932	7.004442e-18	ENSMUSG00000073460	9.406266	1.914644	3.116867e-18	ENSMUSG00000039661	12.299994	4.779174	4.298035e-32
36	ENSMUSG00000029581	8.011342	2.182590	5.759461e-13	ENSMUSG00000041453	9.521667	1.389832	1.005356e-18	ENSMUSG00000058881	8.782569	2.065274	8.718931e-16	ENSMUSG00000028851	7.364143	1.095649	9.710709e-11	ENSMUSG00000025289	5.391623	0.692991	2.241259e-05	ENSMUSG00000025001	9.784193	2.093386	6.530281e-20	ENSMUSG00000027379	9.772845	2.347879	8.241517e-20	ENSMUSG00000113902	10.862775	2.251386	4.452668e-25	ENSMUSG00000003153	8.196815	2.529436	1.337936e-13	ENSMUSG00000022114	6.715042	3.093367	9.770844e-09	ENSMUSG00000053332	8.678504	1.509174	2.365616e-15	ENSMUSG00000032171	7.278724	1.202968	1.987975e-10	ENSMUSG00000006299	7.120519	1.173492	5.587804e-10	ENSMUSG00000019471	9.292415	1.445163	7.004442e-18	ENSMUSG00000025081	9.400823	2.538855	3.193715e-18	ENSMUSG00000029762	12.268587	4.068056	5.949600e-32
37	ENSMUSG00000058558	8.006229	0.989652	5.867465e-13	ENSMUSG00000113061	9.513442	1.482078	1.059504e-18	ENSMUSG00000038587	8.753023	1.935407	1.105784e-15	ENSMUSG00000020307	7.361011	1.331875	9.710709e-11	ENSMUSG00000046814	5.354072	2.401212	2.620289e-05	ENSMUSG00000032387	9.781688	3.193652	6.545273e-20	ENSMUSG00000025630	9.762722	2.373599	8.879649e-20	ENSMUSG00000028957	10.846826	3.353287	5.240303e-25	ENSMUSG00000063229	8.192386	1.827090	1.337936e-13	ENSMUSG00000037805	6.698463	1.274586	1.069130e-08	ENSMUSG00000030795	8.667306	1.219907	2.541502e-15	ENSMUSG00000028832	7.267118	1.148532	2.086697e-10	ENSMUSG00000025403	7.044318	1.908357	9.460309e-10	ENSMUSG00000028345	9.277380	1.639366	7.898522e-18	ENSMUSG00000030007	9.387214	1.039139	3.538764e-18	ENSMUSG00000009941	12.263755	4.680332	6.067684e-32
38	ENSMUSG00000005360	7.965645	2.006265	7.972394e-13	ENSMUSG00000079435	9.457132	1.486953	1.748095e-18	ENSMUSG00000026421	8.740474	2.206711	1.206387e-15	ENSMUSG00000028766	7.305255	1.829136	1.372822e-10	ENSMUSG00000006369	5.350658	1.585433	2.620289e-05	ENSMUSG00000025630	9.764772	2.560727	7.566808e-20	ENSMUSG00000025480	9.730454	3.166425	1.190322e-19	ENSMUSG00000043410	10.824862	3.211410	6.510204e-25	ENSMUSG00000062070	8.191753	1.804410	1.337936e-13	ENSMUSG00000079259	6.689738	2.560926	1.109063e-08	ENSMUSG00000026276	8.663039	1.941446	2.541990e-15	ENSMUSG00000036371	7.265069	1.023036	2.086697e-10	ENSMUSG00000070493	7.037215	0.775652	9.728899e-10	ENSMUSG00000074397	9.274247	2.212958	7.968065e-18	ENSMUSG00000022652	9.352374	2.456949	4.796442e-18	ENSMUSG00000033075	12.256239	2.876153	6.529398e-32
39	ENSMUSG00000022353	7.951904	1.856180	8.714981e-13	ENSMUSG00000030744	9.455868	1.262307	1.748095e-18	ENSMUSG00000035168	8.719818	2.001949	1.382294e-15	ENSMUSG00000028884	7.293353	1.282826	1.466463e-10	ENSMUSG00000055817	5.341441	1.196445	2.683578e-05	ENSMUSG00000039105	9.762267	1.546043	7.591148e-20	ENSMUSG00000022429	9.726342	3.647375	1.209913e-19	ENSMUSG00000062170	10.742764	2.910783	1.472675e-24	ENSMUSG00000060206	8.172772	2.081483	1.529832e-13	ENSMUSG00000079435	6.674032	1.865432	1.155758e-08	ENSMUSG00000091405	8.661440	1.977121	2.541990e-15	ENSMUSG00000042367	7.230250	2.043980	2.630737e-10	ENSMUSG00000019139	6.992012	1.500457	1.314338e-09	ENSMUSG00000070319	9.220998	1.215600	1.259754e-17	ENSMUSG00000051965	9.332233	3.565436	5.518684e-18	ENSMUSG00000036526	12.225904	5.930776	9.309468e-32
40	ENSMUSG00000040511	7.946791	2.234810	8.888925e-13	ENSMUSG00000045128	9.445745	1.053977	1.878650e-18	ENSMUSG00000011256	8.704391	2.032947	1.548667e-15	ENSMUSG00000003528	7.280823	1.451314	1.574342e-10	ENSMUSG00000028470	5.337686	1.167193	2.703689e-05	ENSMUSG00000030884	9.742846	2.042310	9.000506e-20	ENSMUSG00000026027	9.708943	3.032785	1.401929e-19	ENSMUSG00000025586	10.727598	2.564767	1.699669e-24	ENSMUSG00000105039	8.163281	2.125829	1.617393e-13	ENSMUSG00000058600	6.605974	1.809842	1.626363e-08	ENSMUSG00000024949	8.654507	1.246927	2.635459e-15	ENSMUSG00000004151	7.224105	4.161970	2.685322e-10	ENSMUSG00000068856	6.971347	1.175153	1.489553e-09	ENSMUSG00000038845	9.189048	1.471134	1.641757e-17	ENSMUSG00000019179	9.326245	1.254864	5.700380e-18	ENSMUSG00000036598	12.215972	4.181101	1.020397e-31
41	ENSMUSG00000059436	7.916752	1.818874	1.085980e-12	ENSMUSG00000003429	9.442581	1.123376	1.890157e-18	ENSMUSG00000030795	8.686873	1.226509	1.767756e-15	ENSMUSG00000021270	7.267041	0.849673	1.706444e-10	ENSMUSG00000023106	5.321983	0.822359	2.857288e-05	ENSMUSG00000005470	9.715281	2.813024	1.156075e-19	ENSMUSG00000024786	9.686799	3.814904	1.702075e-19	ENSMUSG00000001948	10.711126	2.418876	1.989945e-24	ENSMUSG00000062300	8.144934	1.934803	1.810116e-13	ENSMUSG00000017404	6.564965	1.147014	2.103679e-08	ENSMUSG00000028184	8.587848	2.096848	4.605162e-15	ENSMUSG00000020358	7.190650	1.484769	3.351137e-10	ENSMUSG00000006057	6.964890	1.026076	1.526320e-09	ENSMUSG00000052833	9.180277	1.260312	1.738008e-17	ENSMUSG00000031880	9.314814	3.504511	6.201246e-18	ENSMUSG00000063065	12.215435	2.732449	1.020397e-31
42	ENSMUSG00000057666	7.907166	1.639737	1.149475e-12	ENSMUSG00000053332	9.435621	2.113323	1.972977e-18	ENSMUSG00000060126	8.626475	1.260788	2.938367e-15	ENSMUSG00000024837	7.255138	2.028458	1.824614e-10	ENSMUSG00000022551	5.320617	0.848255	2.857288e-05	ENSMUSG00000055943	9.710270	2.712387	1.190070e-19	ENSMUSG00000024269	9.645675	2.680064	2.432906e-19	ENSMUSG00000023020	10.694131	1.785349	2.343301e-24	ENSMUSG00000058558	8.144301	0.988555	1.810116e-13	ENSMUSG00000060938	6.529191	1.429305	2.577372e-08	ENSMUSG00000045136	8.575049	2.337433	5.027542e-15	ENSMUSG00000051695	7.182457	1.806479	3.475533e-10	ENSMUSG00000028964	6.957786	0.900788	1.571825e-09	ENSMUSG00000056076	9.122642	2.004494	2.857576e-17	ENSMUSG00000007564	9.302837	1.666678	6.783552e-18	ENSMUSG00000024033	12.195571	2.663794	1.265073e-31
43	ENSMUSG00000062300	7.896301	1.917266	1.201532e-12	ENSMUSG00000038900	9.416640	1.850511	2.310442e-18	ENSMUSG00000089774	8.617585	2.032177	3.109474e-15	ENSMUSG00000042367	7.161168	1.927673	3.563273e-10	ENSMUSG00000018770	5.308328	0.597227	2.944930e-05	ENSMUSG00000062773	9.682705	3.790229	1.528481e-19	ENSMUSG00000024758	9.643144	2.416534	2.440603e-19	ENSMUSG00000079317	10.673737	2.986423	2.890632e-24	ENSMUSG00000069601	8.133546	1.890131	1.936165e-13	ENSMUSG00000040952	6.489927	1.411174	3.177048e-08	ENSMUSG00000101355	8.569183	2.081968	5.125918e-15	ENSMUSG00000005566	7.144224	1.528278	4.489700e-10	ENSMUSG00000015869	6.950037	1.370582	1.626739e-09	ENSMUSG00000020432	9.093198	2.870163	3.682557e-17	ENSMUSG00000025651	9.272898	1.198288	8.786862e-18	ENSMUSG00000009090	12.195034	2.927677	1.265073e-31
44	ENSMUSG00000023393	7.896301	2.391323	1.201532e-12	ENSMUSG00000074578	9.407783	2.427437	2.457777e-18	ENSMUSG00000022390	8.595099	2.018835	3.664920e-15	ENSMUSG00000004642	7.111050	1.385396	5.027345e-10	ENSMUSG00000018585	5.252343	0.903922	3.767706e-05	ENSMUSG00000031158	9.679573	2.520765	1.544701e-19	ENSMUSG00000032065	9.629857	2.849061	2.719852e-19	ENSMUSG00000005881	10.642884	2.762885	3.950099e-24	ENSMUSG00000022353	8.110768	1.823349	2.287313e-13	ENSMUSG00000060475	6.448045	2.141384	3.925462e-08	ENSMUSG00000020422	8.567583	2.330751	5.125918e-15	ENSMUSG00000047751	7.131935	1.718151	4.800381e-10	ENSMUSG00000024966	6.937121	0.972735	1.712648e-09	ENSMUSG00000021273	9.088186	1.779879	3.725571e-17	ENSMUSG00000049354	9.270176	1.651214	8.818053e-18	ENSMUSG00000018322	12.192349	2.122135	1.284909e-31
45	ENSMUSG00000081683	7.894383	2.773076	1.201532e-12	ENSMUSG00000028792	9.403354	1.780985	2.507772e-18	ENSMUSG00000045817	8.594053	2.259722	3.664920e-15	ENSMUSG00000017221	7.065318	1.359621	6.858339e-10	ENSMUSG00000002416	5.236299	1.124020	4.010108e-05	ENSMUSG00000035439	9.677693	2.849065	1.544701e-19	ENSMUSG00000002076	9.621633	2.619258	2.886275e-19	ENSMUSG00000039047	10.620921	2.738357	4.951597e-24	ENSMUSG00000026380	8.077868	2.012314	2.892131e-13	ENSMUSG00000038274	6.444555	1.125510	3.954097e-08	ENSMUSG00000026193	8.488126	1.981649	9.964344e-15	ENSMUSG00000030057	7.128521	1.016621	4.813953e-10	ENSMUSG00000064264	6.914520	1.162945	1.970520e-09	ENSMUSG00000028452	9.070019	1.257069	4.328901e-17	ENSMUSG00000025403	9.207030	2.118877	1.557017e-17	ENSMUSG00000054003	12.176243	4.104885	1.539018e-31
46	ENSMUSG00000038900	7.891188	1.582290	1.209878e-12	ENSMUSG00000002477	9.373618	1.732065	3.254921e-18	ENSMUSG00000101355	8.565292	2.161820	4.566236e-15	ENSMUSG00000002985	7.037753	2.041459	8.200424e-10	ENSMUSG00000022913	5.234591	0.815929	4.010108e-05	ENSMUSG00000030180	9.636347	2.463068	2.269639e-19	ENSMUSG00000028617	9.618468	2.490311	2.916892e-19	ENSMUSG00000071470	10.618830	3.190751	4.969151e-24	ENSMUSG00000057666	8.077236	1.628011	2.892131e-13	ENSMUSG00000025508	6.427104	1.848196	4.261342e-08	ENSMUSG00000002329	8.456662	1.922969	1.277758e-14	ENSMUSG00000058135	7.117597	1.754825	5.068793e-10	ENSMUSG00000009894	6.896439	1.640461	2.195981e-09	ENSMUSG00000031781	9.051851	1.662172	5.029692e-17	ENSMUSG00000038803	9.194238	1.242507	1.708036e-17	ENSMUSG00000037570	12.171947	1.984214	1.595160e-31
47	ENSMUSG00000025967	7.884158	1.135527	1.243768e-12	ENSMUSG00000017404	9.356535	0.973381	3.746714e-18	ENSMUSG00000014498	8.482409	2.175339	8.852729e-15	ENSMUSG00000027203	7.033994	1.199811	8.265571e-10	ENSMUSG00000016637	5.231519	1.308357	4.032541e-05	ENSMUSG00000027855	9.630709	3.217265	2.354106e-19	ENSMUSG00000050705	9.613407	2.105385	3.003874e-19	ENSMUSG00000034210	10.589545	2.820344	6.734162e-24	ENSMUSG00000023004	8.063950	1.729181	3.161416e-13	ENSMUSG00000007892	6.426232	1.532440	4.261342e-08	ENSMUSG00000060206	8.451863	1.778304	1.303669e-14	ENSMUSG00000024151	7.115549	1.419635	5.068793e-10	ENSMUSG00000054196	6.871899	2.809532	2.560738e-09	ENSMUSG00000021037	9.045587	1.396583	5.240736e-17	ENSMUSG00000023966	9.192605	2.888978	1.708036e-17	ENSMUSG00000040629	12.161747	3.169278	1.777813e-31
48	ENSMUSG00000024406	7.849645	1.747446	1.597645e-12	ENSMUSG00000046364	9.346412	1.551781	4.038948e-18	ENSMUSG00000101609	8.481101	2.140858	8.852729e-15	ENSMUSG00000020717	7.016767	1.871361	9.177922e-10	ENSMUSG00000027404	5.197041	0.743601	4.701771e-05	ENSMUSG00000000088	9.626323	1.490817	2.412859e-19	ENSMUSG00000002833	9.568486	2.499890	4.522635e-19	ENSMUSG00000021098	10.559999	2.372829	9.144557e-24	ENSMUSG00000099583	8.050663	1.674619	3.456502e-13	ENSMUSG00000032607	6.368644	2.865632	5.862736e-08	ENSMUSG00000020812	8.440131	1.774861	1.411996e-14	ENSMUSG00000027091	7.093701	1.261710	5.816319e-10	ENSMUSG00000002996	6.827342	1.427866	3.432893e-09	ENSMUSG00000063888	9.039322	1.367232	5.461845e-17	ENSMUSG00000045503	9.188522	1.859362	1.721876e-17	ENSMUSG00000026879	12.035580	4.920218	8.132534e-31
49	ENSMUSG00000001918	7.834306	2.044502	1.774152e-12	ENSMUSG00000038274	9.341983	1.123650	4.127324e-18	ENSMUSG00000037857	8.469074	1.767935	9.640974e-15	ENSMUSG00000040945	6.992647	1.446076	1.070503e-09	ENSMUSG00000024661	5.192261	0.491810	4.732284e-05	ENSMUSG00000022034	9.619745	2.388381	2.527124e-19	ENSMUSG00000031352	9.567221	2.497653	4.522635e-19	ENSMUSG00000068823	10.524179	1.642215	1.302630e-23	ENSMUSG00000046434	8.029784	1.826028	4.021282e-13	ENSMUSG00000024067	6.353811	2.046960	6.253170e-08	ENSMUSG00000019979	8.384137	1.719651	2.230686e-14	ENSMUSG00000039660	7.082777	1.755544	6.168040e-10	ENSMUSG00000062867	6.818301	1.029719	3.590679e-09	ENSMUSG00000022300	9.019275	1.688904	6.357997e-17	ENSMUSG00000027510	9.187433	1.950811	1.721876e-17	ENSMUSG00000023967	12.031821	1.855927	8.376291e-31

DE_female_E105 = sc.get.rank_genes_groups_df(gcAnndata, group='female_E10.5', pval_cutoff=0.05, log2fc_min=1.588, key="rank_genes_groups", gene_symbols="external_gene_name").sort_values(by=["scores"], ascending=[False])
DE_female_E115 = sc.get.rank_genes_groups_df(gcAnndata, group='female_E11.5', pval_cutoff=0.05, log2fc_min=1.588, key="rank_genes_groups", gene_symbols="external_gene_name").sort_values(by=["scores"], ascending=[False])
DE_female_E125 = sc.get.rank_genes_groups_df(gcAnndata, group='female_E12.5', pval_cutoff=0.05, log2fc_min=1.588, key="rank_genes_groups", gene_symbols="external_gene_name").sort_values(by=["scores"], ascending=[False])
DE_female_E135 = sc.get.rank_genes_groups_df(gcAnndata, group='female_E13.5', pval_cutoff=0.05, log2fc_min=1.588, key="rank_genes_groups", gene_symbols="external_gene_name").sort_values(by=["scores"], ascending=[False])
DE_female_E145 = sc.get.rank_genes_groups_df(gcAnndata, group='female_E14.5', pval_cutoff=0.05, log2fc_min=1.588, key="rank_genes_groups", gene_symbols="external_gene_name").sort_values(by=["scores"], ascending=[False])
DE_female_E155 = sc.get.rank_genes_groups_df(gcAnndata, group='female_E15.5', pval_cutoff=0.05, log2fc_min=1.588, key="rank_genes_groups", gene_symbols="external_gene_name").sort_values(by=["scores"], ascending=[False])
DE_female_E165 = sc.get.rank_genes_groups_df(gcAnndata, group='female_E16.5', pval_cutoff=0.05, log2fc_min=1.588, key="rank_genes_groups", gene_symbols="external_gene_name").sort_values(by=["scores"], ascending=[False])
sc.tl.rank_genes_groups(gcAnndata, groupby="stage_sex", groups=["female_E16.5"], reference = "female_E13.5", method='wilcoxon', tie_correct=True, key_added="female", pts=True)
DE_female_E165vE135 = sc.get.rank_genes_groups_df(gcAnndata, group="female_E16.5", key="female", pval_cutoff=0.05, log2fc_min=1.588, gene_symbols="external_gene_name")

ranking genes
WARNING: It seems you use rank_genes_groups on the raw count data. Please logarithmize your data before calling rank_genes_groups.
    finished: added to `.uns['female']`
    'names', sorted np.recarray to be indexed by group ids
    'scores', sorted np.recarray to be indexed by group ids
    'logfoldchanges', sorted np.recarray to be indexed by group ids
    'pvals', sorted np.recarray to be indexed by group ids
    'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:00)

sc.tl.rank_genes_groups(gcAnndata_germ, 'stage_sex', method='wilcoxon', use_raw=False)

ranking genes
    finished: added to `.uns['rank_genes_groups']`
    'names', sorted np.recarray to be indexed by group ids
    'scores', sorted np.recarray to be indexed by group ids
    'logfoldchanges', sorted np.recarray to be indexed by group ids
    'pvals', sorted np.recarray to be indexed by group ids
    'pvals_adj', sorted np.recarray to be indexed by group ids (0:00:01)

sc.pl.rank_genes_groups(gcAnndata_germ, n_genes=25, sharey=False, gene_symbols='external_gene_name',)

with pd.ExcelWriter('/users/k2142342/Lab_Sangrithi/working/Mahesh_Sangrithi/GermCell_SmartSeq2/FeatureCounts_OUT_9Feb23/GermCell_DEresults_9Feb23_bystagesex.xlsx') as writer: .to_excel(writer, sheet_name='Female') .to_excel(writer, sheet_name='Male')

gcAnndata_germ

AnnData object with n_obs × n_vars = 774 × 21827
    obs: 'Sample', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'sex', 'stage', 'Embryo', 'celltype', 'stage_sex', 'stage_sex_celltype', 'CellID', 'n_genes', 'n_counts', 'leiden', 'Chr X', 'Chr 9', 'dpt_pseudotime', 'dpt_groups', 'dpt_order', 'dpt_order_indices'
    var: 'geneid', 'start_position', 'end_position', 'chromosome_name', 'external_gene_name', 'mt', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'Gene', 'n_cells', 'highly_variable', 'highly_variable_rank', 'means', 'variances', 'variances_norm'
    uns: 'log1p', 'hvg', 'pca', 'stage_sex_colors', 'neighbors', 'leiden', 'umap', 'tsne', 'leiden_colors', 'Embryo_colors', 'sex_colors', 'stage_colors', 'paga', 'stage_sex_sizes', 'iroot', 'diffmap_evals', 'dpt_changepoints', 'dpt_grouptips', 'female', 'male', 'rank_genes_groups'
    obsm: 'X_pca', 'X_umap', 'X_tsne', 'X_diffmap'
    varm: 'PCs'
    obsp: 'distances', 'connectivities'

gslist = {}
for i in ['male_E9.5', 'female_E9.5', 'male_E10.5', 'female_E10.5', 'male_E11.5', 'female_E11.5', 'male_E12.5', 'female_E12.5', 'male_E13.5', 'female_E13.5', 'male_E14.5', 'female_E14.5', 'male_E15.5', 'female_E15.5', 'male_E16.5', 'female_E16.5']:
    genes = sc.get.rank_genes_groups_df(gcAnndata, 
                                        group = i, 
                                        pval_cutoff=0.01, 
                                        log2fc_min=2,
                                        gene_symbols="external_gene_name",
                                        key="rank_genes_groups").sort_values(by=["scores"], 
                                        ascending=[False]).dropna(axis=0)["external_gene_name"].to_list()
    gslist[i] = genes

top10 = pd.Series(gslist['male_E11.5'][:10] + gslist['male_E12.5'][:10] + gslist['male_E13.5'][:10] + gslist['male_E14.5'][:10] + gslist['male_E15.5'][:10] + gslist['male_E16.5'][:10] + gslist['female_E11.5'][:10] + gslist['female_E12.5'][:10] + gslist['female_E13.5'][:10] + gslist['female_E14.5'][:10] + gslist['female_E15.5'][:10] + gslist['female_E16.5'][:10], name = 'top10')
top10.drop_duplicates()

0                Uty
1              Vdac1
2            Hnrnpa3
3               Dlc1
4             Dpysl2
           ...      
115             H1f0
116            Sycp3
117            Ccnb3
118            Spo11
119    1700029J07Rik
Name: top10, Length: 107, dtype: object

sns.set_context("paper", font_scale=1, rc={"font.size":7,"axes.labelsize":7,"axes.titlesize":14})
d = sc.pl.matrixplot(gcAnndata_germ,
                     top10.drop_duplicates(), 
                     cmap = heatcolors_wr, 
                     groupby = "stage_sex", 
                     standard_scale="var", 
                     gene_symbols='external_gene_name', 
                     edgecolor="white", 
                     linewidths=-0,
                     swap_axes = True, 
                     figsize = [4,9]#,
                     #save = "male_female_top10_horiz.pdf",
                     )

gcAnndata_germ

AnnData object with n_obs × n_vars = 774 × 21827
    obs: 'Sample', 'n_genes_by_counts', 'total_counts', 'total_counts_mt', 'pct_counts_mt', 'sex', 'stage', 'Embryo', 'celltype', 'stage_sex', 'stage_sex_celltype', 'CellID', 'n_genes', 'n_counts', 'leiden', 'Chr X', 'Chr 9', 'dpt_pseudotime', 'dpt_groups', 'dpt_order', 'dpt_order_indices'
    var: 'geneid', 'start_position', 'end_position', 'chromosome_name', 'external_gene_name', 'mt', 'n_cells_by_counts', 'mean_counts', 'pct_dropout_by_counts', 'total_counts', 'Gene', 'n_cells', 'highly_variable', 'highly_variable_rank', 'means', 'variances', 'variances_norm'
    uns: 'log1p', 'hvg', 'pca', 'stage_sex_colors', 'neighbors', 'leiden', 'umap', 'tsne', 'leiden_colors', 'Embryo_colors', 'sex_colors', 'stage_colors', 'paga', 'stage_sex_sizes', 'iroot', 'diffmap_evals', 'dpt_changepoints', 'dpt_grouptips', 'female', 'male', 'rank_genes_groups'
    obsm: 'X_pca', 'X_umap', 'X_tsne', 'X_diffmap'
    varm: 'PCs'
    obsp: 'distances', 'connectivities'